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  VOLUME = {2865},
  YEAR = {2003},
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		  ftp://ftp.neuroinformatik.rub.de/pub/manuscripts/articles/BastianSchonerRiehle2003.pdf}
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@BOOK{beer+al1993,
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  YEAR = 1993,
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		  and Robotics},
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  ADDRESS = {New York}
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@ARTICLE{beer2000,
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  YEAR = 2000,
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  VOLUME = {4(3)},
  PAGES = {91-99.}
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  PAGES = {169--185},
  PUBLISHER = {MIT Press,Cambridge, MA}
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@INPROCEEDINGS{beer91,
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  EDITOR = {Jean-Arcady Meyer and Stewart W. Wilson},
  YEAR = 1991,
  TITLE = {The Neural Basis of Behavioral Choice in an Artifical
		  Insect},
  BOOKTITLE = {From animals to animats},
  PAGES = {247--254},
  ORGANIZATION = {First International Conference on Simulation of Adaptive
		  Behavior}
}

@ARTICLE{beer95,
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  YEAR = 1995,
  TITLE = {A dynamical systems perspective on agent-environment
		  interaction},
  JOURNAL = {Artif. Intell.},
  VOLUME = 72,
  NUMBER = {1-2},
  PAGES = {173--215},
  PUBLISHER = {Elsevier Science Publishers Ltd.},
  ADDRESS = {Essex, UK},
  ISSN = {0004-3702},
  DOI = {http://dx.doi.org/10.1016/0004-3702(94)00005-L}
}

@ARTICLE{beer97:bia,
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		  Roy E. Ritzmann},
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  TITLE = {Biologically Inspired Approaches to Robotics},
  JOURNAL = {Communications of the ACM},
  VOLUME = 40,
  NUMBER = 3,
  PAGES = {30--38}
}

@ARTICLE{beerquinnhillel1997,
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		  Roy E. Ritzmann},
  YEAR = 1997,
  TITLE = {Biologically inspired approaches to robotics: what can we
		  learn from insects?},
  JOURNAL = {Commun. ACM},
  VOLUME = 40,
  NUMBER = 3,
  PAGES = {30--38},
  PUBLISHER = {ACM Press},
  ISSN = {0001-0782},
  DOI = {http://doi.acm.org/10.1145/245108.245118}
}

@ARTICLE{berglundgentz02,
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  YEAR = 2002,
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		  with additive noise},
  JOURNAL = {Probab. Theory Related Fields},
  VOLUME = 122,
  PAGES = {341-388}
}

@BOOK{berkeley09,
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		  feature maps},
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		  Conference on Intelligent Robots and Systems. Intelligent
		  Robots for Flexibility (Cat. No.93CH3213-6)},
  VOLUME = 1,
  PAGES = {553--60},
  PUBLISHER = {IEEE},
  ADDRESS = {New York, NY, USA},
  OPTORGANIZATION = {Gruppe Interaktive Planuungstechnik, Forschungszentrum
		  Inf., Karlsruhe, Germany}
}

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@INBOOK{berthouze04,
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  TITLE = {Robot Bouncing: On the Synergy Between Neural and
		  Body-Environment Dynamics},
  BOOKTITLE = {Embodied Artificial Intelligence},
  EDITION = {Embodied Artificial Intelligence},
  SERIES = {Lecture Notes in Computer Science},
  VOLUME = 3139,
  PAGES = {86--97},
  PUBLISHER = {Springer Verlag}
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@ARTICLE{bialek2001,
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  YEAR = 2001,
  TITLE = {Predictability, complexity and learning},
  JOURNAL = {Neural Computation},
  VOLUME = 13,
  PAGES = 2409,
  URL = {
		  http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:physics/0007070}
}

@ARTICLE{bienenstocklehmann99,
  AUTHOR = {Elie Bienenstock and Daniel Lehmann},
  YEAR = 1998,
  TITLE = {Regulated Criticality in the Brain?},
  JOURNAL = {Advances in Complex Systems},
  VOLUME = 1,
  PAGES = {361-384},
  ABSTRACT = {We propose that a regulation mechanism based on Hebbian
		  covariance plasticity may cause the brain to operate near
		  criticality. We analyze the effect of such a regulation on
		  the dynamics of a network with excitatory and inhibitory
		  neurons and uniform connectivity within and across the two
		  populations. We show that, under broad conditions, the
		  system converges to a critical state lying at the common
		  boundary of three regions in parameter space; these
		  correspond to three modes of behavior: high activity, low
		  activity, oscillation.}
}

@BOOK{binder88monte,
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  YEAR = 1988,
  TITLE = {Monte Carlo Simulation in Statistical Mechanics},
  PUBLISHER = {Springer-Verlag},
  ADDRESS = {Berlin}
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@ARTICLE{bipoo01,
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  TITLE = {Synaptic modification by correlated activity: Hebb's
		  postulate revisited.},
  JOURNAL = {Annu Rev Neurosci},
  VOLUME = 24,
  PAGES = {139-66}
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  AUTHOR = {E. Bloedorn and R. S. Michalski},
  YEAR = 1991,
  TITLE = {Data-driven constructive induction in {AQ17-PRE}: {A}
		  method and experiments},
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		  Artificial Intelligence},
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  PUBLISHER = {IEEE Computer Society Press},
  ADDRESS = {Los Alamitos, CA}
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@MISC{borel97,
  AUTHOR = {BOREL},
  YEAR = 1997,
  TITLE = {BOulder REinforcement Learning (BOREL) Group, {U}niversity
		  of {C}olorado, {B}oulder: Java Demonstrations of
		  Reinforcement Learning},
  HOWPUBLISHED = {\url{http://www.cs.colorado.edu/~baveja/borel.html}}
}

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  YEAR = 1984,
  TITLE = {Vehicles: {E}xperiments in Synthetic Psychology},
  PUBLISHER = {MIT Press}
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@ARTICLE{bredenfeld01,
  AUTHOR = {Ansgar Bredenfeld and Herbert Jaeger and Thomas
		  Christaller},
  YEAR = 2001,
  TITLE = {Mobile Robots with Dual Dynamics},
  JOURNAL = {ERCIM News},
  VOLUME = 42
}

@TECHREPORT{brooks86,
  AUTHOR = {Rodney Brooks},
  MONTH = MAY,
  YEAR = 1986,
  TITLE = {Achieving Artificial Intelligence through Building
		  Robots},
  PAGES = 12,
  ADDRESS = {Cambridge, Massachusetts},
  INSTITUTION = {Artificial Intelligence Laboratory, Massachusetts
		  Institute of Technology (MIT)},
  ABSTRACT = {We argue that generally accepted methodologies of
		  artificial intelligence research are limited in the
		  proportion of human level intelligence they can be expected
		  to emulate. We argue that the currently accepted
		  decompositions and static representations used in such
		  research are wrong. We argue for a shift to a process based
		  model, with a decomposition based on task achieving
		  behaviors as the organizational principle. In particular we
		  advocate building robotic insects.}
}

@INPROCEEDINGS{brooks87,
  AUTHOR = {Rodney Brooks},
  MONTH = JUN,
  YEAR = 1987,
  TITLE = {Intelligence without representation},
  BOOKTITLE = {Workshop on Foundations of AI}
}

@TECHREPORT{brooks90,
  AUTHOR = {Rodney Brooks},
  MONTH = APR,
  YEAR = 1990,
  TITLE = {The Behavior Language: User's Guide},
  PAGES = 35,
  ADDRESS = {Cambridge, Massachusetts},
  INSTITUTION = {Artificial Intelligence Laboratory, Massachusetts
		  Institute of Technology (MIT)},
  ABSTRACT = {The Behavior Language is a rule-based real-time parallel
		  robot programming language originally based on ideas from
		  [Brooks 86], [Connell 89], and [Maes 89]. It compiles into
		  a modified and extended version of the subsumption
		  architecture [Brooks 86] and thus has backends for a number
		  of processors including the Motorola 68000 and 68HC11, the
		  Hitachi 6301, and Common Lisp. Behaviors are groups of
		  rules which are activatable by a number of different
		  schemes. There are no shared data structures across
		  behaviors, but instead all communication is by explicit
		  message passing. All rules are assumed to run in parallel
		  and asynchronously. It includes the earlier notions of
		  inhibition and suppression, along with a number of
		  mechanisms for spreading of activation.}
}

@MISC{brooks99cambrian,
  AUTHOR = {R. Brooks},
  YEAR = 1999,
  TITLE = {Cambrian Intelligence: The Early History of the New AI},
  TEXT = {R. Brooks. Cambrian Intelligence: The Early History of the
		  New AI. Cambridge: MIT Press, 1999.}
}

@TECHREPORT{brooksstein93,
  AUTHOR = {Rodney Brooks and Lynn A. Stein},
  MONTH = AUG,
  YEAR = 1993,
  TITLE = {Building Brains for Bodies},
  PAGES = 16,
  ADDRESS = {Cambridge, Massachusetts},
  INSTITUTION = {Artificial Intelligence Laboratory, Massachusetts
		  Institute of Technology (MIT)},
  ABSTRACT = {We describe a project to capitalize on newly available
		  levels of computational resources in order to understand
		  human cognition. We will build an integrated physical
		  system including vision, sound input and output, and
		  dextrous manipulation, all controlled by a continuously
		  operating large scale parallel MIMD computer. The resulting
		  system will learn to ``think'' by building on its bodily
		  experiences to accomplish progressively more abstract
		  tasks. Past experience suggests that in attempting to build
		  such an integrated system we will have to fundamentally
		  change the way artificial intelligence, cognitive science,
		  linguistics, and philosophy think about the organization of
		  intelligence. We expect to be able to better reconcile the
		  theories that will be developed with current work in
		  neuroscience.}
}

@INPROCEEDINGS{buchli05,
  AUTHOR = {Buchli, J. and Righetti, L. and Ijspeert, A.J.},
  YEAR = 2005,
  TITLE = {A dynamical systems approach to learning: a
		  frequency-adaptive hopper robot},
  BOOKTITLE = {Proceedings of the {VIII}th European Conference on
		  Artificial Life {ECAL} 2005},
  SERIES = {Lecture Notes in Artificial Intelligence},
  PAGES = {210--220},
  PUBLISHER = {Springer Verlag},
  TYPE = {conference},
  PDF = {\url{http://birg2.epfl.ch/publications/fulltext/buchli05.pdf}}
}

@INPROCEEDINGS{buehrmann_al2004,
  AUTHOR = {Buehrmann, T. and Di Paolo, E.},
  YEAR = 2004,
  TITLE = {Closing the loop: evolving a model-free visually guided
		  robot arm.},
  BOOKTITLE = {Proceedings of the ninth international conference on the
		  simulation and synthesis of living systems},
  VOLUME = 9,
  PUBLISHER = {MIT Press}
}

@MASTERSTHESIS{bunk04,
  AUTHOR = {Michael Bunk},
  YEAR = 2004,
  TITLE = {Echtzeitf\"ahige Modellbasierte Bilderkennung Zur
		  Visuomotorischen Kontrolle Bewegter Objekte},
  SCHOOL = {Universit\"at Leipzig}
}

@ARTICLE{burrone03,
  AUTHOR = {J. Burrone and V. N. Murthy},
  YEAR = 2003,
  TITLE = {Synaptic Gain Control and Homeostasis},
  JOURNAL = {Current Opinion in Neurobiology},
  VOLUME = 13,
  PAGES = {560--567}
}

@INPROCEEDINGS{cangelosi04sab,
  AUTHOR = {A. Cangelosi},
  EDITOR = {S. Schaal et al.},
  MONTH = {July},
  YEAR = 2004,
  TITLE = {The sensorimotor bases of linguistic structure:
		  Experiments with grounded adaptive agents},
  BOOKTITLE = {SAB04},
  PAGES = {487-496},
  NOTE = {Proceedings of the Eighth International Conference on the
		  Simulation of Adaptive Behaviour: From Animals to Animats
		  8},
  PUBLISHER = {Cambridge MA, MIT Press},
  ADDRESS = {Los Angeles},
  HOWPUBLISHED = {\url{http://www.isrl.uiuc.edu/~amag/langev/paper/cangelosi04sab.html}}
}

@BOOK{cannon39,
  AUTHOR = {W. B. Cannon},
  YEAR = 1939,
  TITLE = {The Wisdom of the Body},
  PUBLISHER = {Norton},
  ADDRESS = {New {Y}ork}
}

@INPROCEEDINGS{chechik00temporally,
  AUTHOR = {Gal Chechik and Naftali Tishby},
  YEAR = 2000,
  TITLE = {Temporally Dependent Plasticity: An Information Theoretic
		  Account},
  BOOKTITLE = {{NIPS}},
  PAGES = {110-116}
}

@BOOK{chu:introduction,
  AUTHOR = {C. K. Chui},
  YEAR = 1992,
  TITLE = {An Introduction to Wavelets},
  SERIES = {Wavelet Analysis and its Applications},
  VOLUME = 1,
  PUBLISHER = {Academic Press, Inc.},
  ABSTRACT = {This is the first volume in the series WAVELET ANALYSIS
		  AND ITS APPLICATIONS. It is an introductory treatise on
		  wavelet analysis, with an emphasis on spline wavelets and
		  and time-frequency analysis. Among the basic topics covered
		  are time frequency localization, intergral wavelet
		  transforms, dyadic wavelets, frames, spine wavelets,
		  orthonormal wavelet bases, and wavelet packets. Is is
		  suitable as a textbook for a beginning course on wavelet
		  analysis and is directed toward both mathematicians and
		  engineers who wish to learn about the subject.},
  KEYWORDS = {An Overview, Fourier Analysis, Wavelet Transforms and
		  Time-frequency Analysis, Cardinal Spline Analysis, scaling
		  functions and Wavelets, Cardinal Spline Wavelets,
		  Orthogonal Wavelets and Wavelet Packets},
  ISBN = {0-12-174584-8}
}

@BOOK{chua98,
  AUTHOR = {L. O. Chua},
  YEAR = 1998,
  TITLE = {{CNN}: {A} paradigm for complexity},
  PUBLISHER = {World Scientific}
}

@TECHREPORT{cliff92a,
  AUTHOR = {D. Cliff and P. Husbands and I. Harvey},
  YEAR = 1992,
  TITLE = {Analysis of evolved sensory-motor controllers},
  NUMBER = {Cognitive Science Research Paper CSRP264},
  ADDRESS = {Brighton BN1 9QH, England, UK},
  INSTITUTION = {School of Cognitive and Computing Sciences, University of
		  Sussex}
}

@ARTICLE{cohen92,
  AUTHOR = {M. Cohen},
  YEAR = 1992,
  TITLE = {{The} Construction of Arbitrary Stable Dynamics in
		  Nonlinear Neural Networks},
  JOURNAL = {Neural Networks},
  VOLUME = 5,
  PAGES = {83-103}
}

@BOOK{cohenfeig86,
  AUTHOR = {P. R. Cohen and E. W. Feigenbaum},
  YEAR = 1986,
  TITLE = {The Handbook of Artificial Intelligence Vol. 3},
  PUBLISHER = {Addison Wesley}
}

@ARTICLE{collins01,
  AUTHOR = {Collins, S.H. and Wisse, M. and Ruina, A.},
  YEAR = 2001,
  TITLE = {A 3-D Passive Dynamic Walking Robot with Two Legs and
		  Knees},
  JOURNAL = {International Journal of Robotics Research},
  VOLUME = {20 (7)},
  PAGES = {607-615}
}

@ARTICLE{collins05,
  AUTHOR = {Collins, Steven H. and Ruina, Andy and Tedrake, Russ and
		  Wisse, Martijn},
  YEAR = 2005,
  TITLE = {Efficient Bipedal Robots Based on Passive-Dynamic
		  Walkers},
  JOURNAL = {Science},
  VOLUME = 307,
  PAGES = {1082-1085}
}

@INPROCEEDINGS{conf/icann/porrw01,
  AUTHOR = {Bernd Porr and Florentin W{\"o}rg{\"o}tter},
  YEAR = 2001,
  TITLE = {Temporal Hebbian Learning in Rate-Coded Neural Networks:
		  {A} Theoretical Approach towards Classical Conditioning},
  BOOKTITLE = {ICANN},
  PAGES = {1115--1120},
  BIBDATE = {2002-01-03},
  BIBSOURCE = {DBLP,
		  http://dblp.uni-trier.de/db/conf/icann/icann2001.html#PorrW01},
  URL = {
		  http://link.springer.de/link/service/series/0558/bibs/2130/21301115.htm}
}

@INPROCEEDINGS{conf/nips/porrw02,
  AUTHOR = {Bernd Porr and Florentin W{\"o}rg{\"o}tter},
  YEAR = 2002,
  TITLE = {Learning a Forward Model of a Reflex},
  BOOKTITLE = {NIPS},
  PAGES = {1531--1538},
  BIBDATE = {2004-10-12},
  BIBSOURCE = {DBLP,
		  http://dblp.uni-trier.de/db/conf/nips/nips2002.html#PorrW02},
  HOWPUBLISHED = {\url{http://books.nips.cc/papers/files/nips15/CN02.pdf}}
}

@BOOK{craig89,
  AUTHOR = {J. C. Craig},
  YEAR = 1989,
  TITLE = {Introduction to Robotics, 2nd Edition},
  NOTE = {2nd Edition 1991},
  PUBLISHER = {Addison Wesley}
}

@ARTICLE{cruse03,
  AUTHOR = {Holk Cruse},
  YEAR = 2003,
  TITLE = {The Evolution of Cognition - a Hypothesis},
  JOURNAL = {. Cognitive Science},
  VOLUME = {27(1)},
  PAGES = {135-155}
}

@INCOLLECTION{cs0751.68052ma,
  AUTHOR = {Gutknecht, Matthias and Pfeifer, Rolf and Stolze, Markus},
  TITLE = {{Cooperative hybrid systems.}},
  BOOKTITLE = {{Artificial intelligence, IJCAI-91, Proc. 12th Int. Conf.,
		  Sydney/Australia 1991, 824-829 }},
  ABSTRACT = {{[For the entire collection see Zbl. 741.68016.]\par The
		  authors' effort to build a situated knowledge-based system
		  resultet in a cooperative hybrid system called DDT ( device
		  diagnostic tool). DDT is a hybrid symbolic/connectionist
		  system, embodying cooperativity and self-tuning
		  capabilities, thus being able to face the problem of model
		  explosion cycle. The approach is illustrated using a
		  real-life expert system in the domain of technical
		  troubleshooting.}},
  KEYWORDS = {{situated knowledge-based system; device diagnostic
		  tool}},
  LANGUAGE = {English},
  REVIEWER = {{N.Curteanu (Iasi)}},
  CLASSMATH = {{*I.2.6 Learning I.2.8 Problem Solving, Control Methods,
		  and Search G.4 Mathematical Software I.2.1 Applications and
		  Expert Systems to Artificial Intelligence}}
}

@BOOK{cs0802.00041ma,
  AUTHOR = {Steels, Luc (ed.) and Schreiber, Guus (ed.) and van de
		  Velde, Walter (ed.)},
  TITLE = {{A future for knowledge acquisition. 8th European
		  knowledge acquisition workshop, EKAW '94, Hoegaarden,
		  Belgium, September 26-29, 1994. Proceedings.}},
  PUBLISHER = {{Lecture Notes in Computer Science. Lecture Notes in
		  Artificial Intelligence. 867. Berlin: Springer-Verlag. xii,
		  413 p. DM 80.00; \"oS 624.00; sFr 80.00 }},
  ABSTRACT = {{The articles of this volume will be reviewed
		  individually.}},
  KEYWORDS = {{Hoegaarden (Belgium); Workshop; Proceedings; EKAW '94;
		  Knowledge acquisition}},
  LANGUAGE = {English},
  CLASSMATH = {{*A.0 General (Literature)}}
}

@ARTICLE{cs0909.68182ma,
  AUTHOR = {Steels, Luc},
  TITLE = {{The origins of syntax in visually grounded robotic
		  agents.}},
  JOURNAL = {Artif. Intell.},
  VOLUME = 103,
  NUMBER = {1-2},
  PAGES = {133-156},
  ABSTRACT = {{The paper proposes a set of principles and a general
		  architecture that may explain how language and meaning may
		  originate and complexify in a group of physically grounded
		  distributed agents. An experimental setup is introduced for
		  concretising and validating specific mechanisms based on
		  these principles. The setup consists of two robotic heads
		  that watch static or dynamic scenes and engage in language
		  games, in which one robot describes to the other what they
		  see. The first results from experiments showing the
		  emergence of distinctions, of a lexicon, and of primitive
		  syntactic structures are reported.}},
  KEYWORDS = {{symbolic models; robotic agents; origins of language and
		  meaning}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.m Miscellaneous (Artificial Intelligence)}}
}

@ARTICLE{cs1005.68732ma,
  AUTHOR = {Kim, Dong-Han and Kim, Jong-Hwan},
  TITLE = {{A real-time limit-cycle navigation method for fast mobile
		  robots and its application to robot soccer.}},
  JOURNAL = {Robot. Auton. Syst.},
  VOLUME = 42,
  NUMBER = 1,
  PAGES = {17-30},
  ABSTRACT = {{Summary: A mobile robot should be designed to navigate
		  with collision avoidance capability in the real world,
		  flexibly coping with the changing environment. In this
		  paper, a novel limit-cycle navigation method is proposed
		  for a fast mobile robot using the limit-cycle
		  characteristics of a 2nd-order nonlinear function. It can
		  be applied to the robot operating in a dynamically changing
		  environment, such as in a robot soccer system. By adjusting
		  the radius of the motion circle and the direction of
		  obstacle avoidance, the navigation method proposed enables
		  a robot to maneuver smoothly towards any desired
		  destination. Simulations and real experiments using a robot
		  soccer system demonstrate the merits and practical
		  applicability of the proposed method.}},
  KEYWORDS = {{robot soccer; navigation; limit cycle; real-time
		  control}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.9 Robotics}}
}

@INCOLLECTION{cs1013.68668ma,
  AUTHOR = {Lichtensteiger, Lukas and Pfeifer, Rolf},
  TITLE = {{An optimal sensor morphology improves adaptability of
		  neural network controllers.}},
  BOOKTITLE = {{Dorronsoro, Jos\'e R. (ed.), Artificial neural networks -
		  ICANN 2002. 12th international conference, Madrid, Spain,
		  August 28-30, 2002. Proceedings. Berlin: Springer. Lect.
		  Notes Comput. Sci. 2415, 850-855 }},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning}}
}

@INCOLLECTION{cs1032.68706ma,
  AUTHOR = {Steels, Luc},
  TITLE = {{The evolution of communication systems by adaptive
		  agents.}},
  BOOKTITLE = {{Alonso, Eduardo (ed.) et al., Adaptive agents and
		  multi-agent systems. Adaptation and multi-agent learning.
		  Berlin: Springer. Lect. Notes Comput. Sci. 2636, 125-140
		  }},
  ABSTRACT = {{Summary: The paper surveys some of the mechanisms that
		  have been demonstrated to be relevant for evolving
		  communication systems in software simulations or robotic
		  experiments. In each case, precursors or parallels with
		  work in the study of artificial life and adaptive behaviour
		  are discussed.}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning}}
}

@INCOLLECTION{cs1037.68739ma,
  AUTHOR = {te Boekhorst, Rene and Lungarella, Max and Pfeifer, Rolf},
  TITLE = {{Dimensionality reduction through sensory-motor
		  coordination.}},
  BOOKTITLE = {{Kaynak, Okyay (ed.) et al., Artificial neural networks
		  and neural information processing -- ICANN/ICONIP 2003.
		  Joint international conference ICANN/ICONIP 2003, Istanbul,
		  Turkey, 26-29, 2003. Proceedings. Berlin: Springer. Lect.
		  Notes Comput. Sci. 2714, 496-503 }},
  ABSTRACT = {{Summary: The problem of category learning has been
		  traditionally investigated by employing disembodied
		  categorization models. One of the basic tenets of embodied
		  cognitive science states that categorization can be
		  interpreted as a process of sensory-motor coordination, in
		  which an embodied agent, while interacting with its
		  environment, can structure its own input space for the
		  purpose of learning about categories. Many researchers,
		  including John Dewey and Jean Piaget, have argued that
		  sensory-motor coordination is crucial for perception and
		  for development. In this paper we give a quantitative
		  account of why sensory-motor coordination is important for
		  perception and category learning.}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning}}
}

@ARTICLE{cs2000.02769dr,
  AUTHOR = {Nolfi, Stefano and Tani, Jun},
  YEAR = 1999,
  TITLE = {{Extracting regularities in space and time through a
		  cascade of prediction networks: The case of a mobile robot
		  navigating in a structured environment.}},
  JOURNAL = {Connection Science, Journal of Neural Computing,
		  Artificial Intelligence and Cognitive Research},
  VOLUME = 11,
  NUMBER = 2,
  PAGES = {125-148},
  ABSTRACT = {{Summary: We propose that the ability to extract
		  regularities from time series through prediction learning
		  can be enhanced if we use a hierarchical architecture in
		  which higher layers are trained to predict the internal
		  state of lower layers when such states change
		  significantly. This hierarchical organization has two
		  functions: (a) it forces the system to recode sensory
		  information progressively so as to enhance useful
		  regularities and filter out useless information; and (b) it
		  progressively reduces the length of the sequences which
		  should be predicted going from lower to higher layers.
		  This, in turn, allows higher levels to extract higher-level
		  regularities which are hidden at the sensory level. By
		  training an architecture of this type to predict the next
		  sensory state of a robot navigating in an environment
		  divided in two rooms, we show how the first-level
		  prediction layer extracts low-level regularities, while the
		  second-level prediction layer extracts higher-level
		  regularities.}},
  KEYWORDS = {{prediction learning; development; mobile robotics}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning I.2.9 Robotics}}
}

@ARTICLE{cs2003.030202c,
  AUTHOR = {Steels, Luc and Baillie, Jean-Christophe},
  TITLE = {{Shared grounding of event descriptions by autonomous
		  robots.}},
  JOURNAL = {Robotics and Autonomous Systems.},
  VOLUME = 43,
  NUMBER = {2-3},
  PAGES = {163-173},
  ABSTRACT = {{Summary: The paper describes a system for open-ended
		  communication by autonomous robots about event descriptions
		  anchored in reality through the robot's sensori-motor
		  apparatus. The events are dynamic and agents must
		  continually track changing situations at multiple levels of
		  detail through their vision system. We are specifically
		  concerned with the question how grounding can become shared
		  through the use of external (symbolic) representations,
		  such as natural language expressions.}},
  KEYWORDS = {{Autonomous Robots; Event Descriptions; Open-Ended}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.9 Robotics I.2.1 Applications and Expert Systems to
		  Artificial Intelligence}}
}

@ARTICLE{cs2003.120099c,
  AUTHOR = {Tani, Jun},
  TITLE = {{Learning to generate articulated behavior through the
		  bottom-up and the top-down interaction processes.}},
  JOURNAL = {Neural Networks. The Official Journal of the International
		  Neural Network Society, European Neural Network Society,
		  Japanese Neural Network Society.},
  VOLUME = 16,
  NUMBER = 1,
  PAGES = {11-23},
  ABSTRACT = {{Summary: A novel hierarchical neural network architecture
		  for sensory-motor learning and behavior generation is
		  proposed. Two levels of forward model neural networks are
		  operated on different time scales while parametric
		  interactions are allowed between the two network levels in
		  the bottom-up and top-down directions. The models are
		  examined through experiments of behavior learning and
		  generation using a real robot arm equipped with a vision
		  system. The results of the learning experiments showed that
		  the behavioral patterns are learned by self-organizing the
		  behavioral primitives in the lower level and combining the
		  primitives sequentially in the higher level. The results
		  contrast with prior work by Pawelzik et al. [Neural
		  Comput. 8, 340 (1996)], Tani and Nolfi
		  [From animals to animats, 1998], and
		  Wolpert and Kawato [Neural Networks 11, 1317
		  (1998)] in that the primitives are represented in a
		  distributed manner in the network in the present scheme
		  whereas, in the prior work, the primitives were localized
		  in specific modules in the network. Further experiments of
		  on-line planning showed that the behavior could be
		  generated robustly against a background of real world noise
		  while the behavior plans could be modified flexibly in
		  response to changes in the environment. It is concluded
		  that the interaction between the bottom-up process of
		  recalling the past and the top-down process of predicting
		  the future enables both robust and flexible situated behavior.}},
  KEYWORDS = {{Learning; Behavior; Articulation; Chunking}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning F.1.1 Models of Computation J.3 Life and
		  Medical Sciences (Computer Applications)}}
}

@INCOLLECTION{cs2004.019004g,
  AUTHOR = {Pfeifer, Rolf},
  TITLE = {{Embodied Artificial Intelligence - On the role of
		  morphology and materials in the emergence of cognition}},
  BOOKTITLE = {{Schubert, Sigrid E.; Reusch, Bernd; Jesse, Norbert
		  (eds.). Informatik bewegt: Informatik 2002 - 32.
		  Jahrestagung der Gesellschaft f\"ur Informatik e.v. (GI),
		  30. September - 3.Oktober 2002 in Dortmund. Bonn: K\"ollen
		  Verlag. Lect. Notes Inform. 19, p. 49-63 }},
  ABSTRACT = {{Entire collection CS 2004.019000G}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.0 General (Artificial Intelligence)}}
}

@INCOLLECTION{cs9603.04321rr,
  AUTHOR = {Steels, Luc},
  TITLE = {{Equilibrium analysis of behavior systems.}},
  BOOKTITLE = {{Cohn, A. G. (ed.). 11th European Conference on Artificial
		  Intelligence. Proceedings. (ECAI '94, Amsterdam, NL,
		  Aug.8-12, 1994). Chichester: John Wiley & Sons. 832 p.,
		  714-718 p. }},
  ABSTRACT = {{A behavior system consists of the components and control
		  programs necessary to establish a particular behavior in a
		  robotic agent. The paper proposes a mathematical approach
		  for the analysis of behavior systems. The approach rests on
		  viewing a behavior system as a dynamical system whose
		  equilibrium state is attained when the behavior it is
		  responsible for is achieved.}},
  KEYWORDS = {{behavior system; robotic agent; dynamical system;
		  equilibrium state; autonomous agent}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.9 Robotics}}
}

@INCOLLECTION{cs9709.07022rr,
  AUTHOR = {Tani, Jun},
  TITLE = {{Self-organizing of symbolic processes through interaction
		  with the physical world.}},
  BOOKTITLE = {{Mellish, Chris S. (ed.). IJCAI-95. 14th international
		  joint conference on artificial intelligence. Proceedings.
		  (IJCAI-95. 14th International Joint Conference on
		  Artificial Intelligence, Montreal (Canada), 20-25 Aug
		  1995). San Mateao, CA: Morgan Kaufmann Publishers, Inc..
		  XLIV, 2077 p., p. 112-118 }},
  ABSTRACT = {{The paper describes how symbolic processes are
		  self-organized in the navigational learning of a mobile
		  robot. Based on a dynamical system's approach, the paper
		  shows that the forward modeling scheme based on recurrent
		  neural network (RNN) learning is capable of extracting
		  grammatical structure hidden in the geometry of the
		  workspace from navigational experience. This robot is
		  capable of mentally simulating its own actions using the
		  acquired forward model. The paper shows that such a mental
		  process by the RNN can naturally be situated with respect
		  to the behavioural contexts, provided that the forward
		  model learned is embedded on the global attractor. The
		  internal representation obtained is proved to be grounded,
		  since it is self-organized solely through itnteraction with
		  the physical world. The paper shows also that structural
		  stability arises in the interaction between the neural
		  dynamics and the environment dynamics, accounting for the
		  situatedness of the internal symbolic process.}},
  KEYWORDS = {{self-organization of symbolic processes; robot
		  navigation; situatedness of symbolic processes}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning I.5.1 Models (Pattern Recognition) I.2.7
		  Natural Language Processing I.2.8 Problem Solving, Control
		  Methods, and Search I.2.5 Programming Languages and
		  Software (Artificial Intelligence) I.2.1 Applications and
		  Expert Systems to Artificial Intelligence I.6.8 Types of
		  Simulation I.2.9 Robotics}}
}

@ARTICLE{cs9710.00648rr,
  AUTHOR = {Tani, Jun and Fukumura, Naohiro},
  TITLE = {{Self-organizing internal representation in learning of
		  navigation: a physical experiment by the mobile robot
		  YAMBICO.}},
  ABSTRACT = {{The paper discusses a novel scheme for sensory-based
		  navigation of a mobile robot. In our previous work (Tani
		  $\&$ Fukumura, 1994, Neural Networks, 7(3), 553--563), we
		  formulated the problem of goal-directed navigation as an
		  embedding problem of dynamical systems: desired
		  trajectories in a task space should be embedded in an
		  adequate sensory-based internal state space so that a
		  unique mapping from the internal state space to the motor
		  command could be established. In the current formulation a
		  recurrent neural network is employed, which shows that an
		  adequate internal state space can be self-organized,
		  through supervised training with sensorimotor sequences.
		  The experiment was conducted using a real mobile robot
		  equipped with a laser range sensor, demonstrating the
		  validity of the presented scheme by working in a noisy
		  real-world environment.}},
  KEYWORDS = {{neural networks; sensory motor system; mobile robot;
		  navigation learning}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning I.2.9 Robotics}}
}

@ARTICLE{dagumluby93,
  AUTHOR = {P. Dagum and M. Luby},
  MONTH = MAR,
  YEAR = 1993,
  TITLE = {Approximating Probabilistic Inference in Bayesian Belief
		  Networks is {NP}-Hard},
  JOURNAL = {Artificial Intelligence},
  VOLUME = 60,
  NUMBER = 1,
  PAGES = {141--153},
  KEY = {Dagum \&{} Luby},
  LOCATION = {CMU Engineering \&{} Science Library}
}

@BOOK{dau:ten,
  AUTHOR = {Ingrid Daubechies},
  YEAR = 1992,
  TITLE = {Ten Lectures on Wavelets},
  SERIES = {CBMS-NSF Regional Conference Series in Applied
		  Mathematics},
  VOLUME = 61,
  PUBLISHER = {Society for Industrial and Applied Mathematics},
  ADDRESS = {Philadelphia},
  URL = {http://www.siam.org/catalog/mcc02/daubechi.htm}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/bakkumsbgdp03,
  AUTHOR = {Douglas J. Bakkum and Alexander C. Shkolnik and Guy
		  Ben-Ary and Phil Gamblen and Thomas B. DeMarse and Steve M.
		  Potter},
  YEAR = 2003,
  TITLE = {Removing Some 'A' from AI: Embodied Cultured Networks.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {130-145},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=130},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/hafner03,
  AUTHOR = {Verena Vanessa Hafner},
  YEAR = 2003,
  TITLE = {Agent-Environment Interaction in Visual Homing.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {180-185},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=180},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/holland03,
  AUTHOR = {Owen Holland},
  YEAR = 2003,
  TITLE = {The Future of Embodied Artificial Intelligence: Machine
		  Consciousness?.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {37-53},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=37},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/hosoda03,
  AUTHOR = {Koh Hosoda},
  YEAR = 2003,
  TITLE = {Robot Finger Design for Developmental Tactile Interaction:
		  Anthropomorphic Robotic Soft Fingertip with Randomly
		  Distributed Receptors.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {219-230},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=219},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/iidap03,
  AUTHOR = {Fumiya Iida and Rolf Pfeifer},
  YEAR = 2003,
  TITLE = {Self-Stabilization and Behavioral Diversity of Embodied
		  Adaptive Locomotion.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {119-129},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=119},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/ishigurok03,
  AUTHOR = {Akio Ishiguro and Toshihiro Kawakatsu},
  YEAR = 2003,
  TITLE = {How Should Control and Body Systems Be Coupled? A Robotic
		  Case Study.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {107-118},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=107},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/lichtensteiger03,
  AUTHOR = {Lukas Lichtensteiger},
  YEAR = 2003,
  TITLE = {The Need to Adapt and Its Implications for Embodiment.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {98-106},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=98},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/lungarellab03,
  AUTHOR = {Max Lungarella and Luc Berthouze},
  YEAR = 2003,
  TITLE = {Robot Bouncing: On the Synergy Between Neural and
		  Body-Environment Dynamics.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {86-97},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=86},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/muratakkytk03,
  AUTHOR = {Satoshi Murata and Akiya Kamimura and Haruhisa Kurokawa
		  and Eiichi Yoshida and Kohji Tomita and Shigeru Kokaji},
  YEAR = 2003,
  TITLE = {Self-Reconfigurable Robots: Platforms for Emerging
		  Functionality.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {312-330},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=312},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/pfeiferi03,
  AUTHOR = {Rolf Pfeifer and Fumiya Iida},
  YEAR = 2003,
  TITLE = {Embodied Artificial Intelligence: Trends and Challenges.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {1-26},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=1},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/spornsp03,
  AUTHOR = {Olaf Sporns and Teresa K. Pegors},
  YEAR = 2003,
  TITLE = {Information-Theoretical Aspects of Embodied Artificial
		  Intelligence.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {74-85},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=74},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/steels03,
  AUTHOR = {Luc Steels},
  YEAR = 2003,
  TITLE = {The Autotelic Principle.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {231-242},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=231},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{dblp:conf/dagstuhl/ziemke03,
  AUTHOR = {Tom Ziemke},
  YEAR = 2003,
  TITLE = {Embodied AI as Science: Models of Embodied Cognition,
		  Embodied Models of Cognition, or Both?},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {27-36},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=27},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@BOOK{deboeck99,
  AUTHOR = {G. Deboeck and T. Kohonen},
  YEAR = 1999,
  TITLE = {Visual Explorations in Finance with Self-Organizing Maps},
  PUBLISHER = {Springer}
}

@TECHREPORT{deutschmann04,
  AUTHOR = {Giso Deutschmann},
  YEAR = 2004,
  TITLE = {Echtzeitf\"ahige Lernverfahren Zur Situationserkennung Aus
		  Komplexen Sensordaten F\"ur Autonome Roboter},
  NOTE = {Diplomarbeit},
  INSTITUTION = {Universit\"at Leipzig}
}

@ARTICLE{dietterichflann97,
  AUTHOR = {Thomas G. Dietterich and Nicholas S. Flann},
  YEAR = 1997,
  TITLE = {Explanation-Based and Reinforcement Learning: {A} Unified
		  View},
  JOURNAL = {Machine Learning},
  VOLUME = 28,
  NUMBER = {2/3},
  PAGES = {169--210},
  URL = {ftp://ftp.cs.orst.edu/pub/tgd/papers/mlj-ebrl.ps.gz},
  LEARNING = {Reinforcement, EBL},
  GAME = {Chess}
}

@BOOK{dillmann91,
  AUTHOR = {R. Dillmann},
  YEAR = 1991,
  TITLE = {Informationsverarbeitung in der {R}obotik},
  PUBLISHER = {Springer}
}

@INPROCEEDINGS{dipaolo03,
  AUTHOR = {E. Di Paolo},
  EDITOR = {K. Murase and T. Asakura},
  YEAR = 2003,
  TITLE = {Organismically-Inspired Robotics: Homeostatic Adaptation
		  and Natural Teleology Beyond the Closed Sensorimotor Loop},
  BOOKTITLE = {Dynamical Systems Approach to Embodiment and Sociality},
  PAGES = {19 - 42},
  PUBLISHER = {Advanced Knowledge International},
  ADDRESS = {Adelaide}
}

@ARTICLE{dipaolo_ab2002,
  AUTHOR = {Di Paolo, E.},
  YEAR = 2002,
  TITLE = {Spike-Timing Dependent Plasticity for Evolved Robots},
  JOURNAL = {Adaptive Behavior},
  VOLUME = 10,
  NUMBER = {3/4},
  PAGES = {243-263}
}

@ARTICLE{dipaolo_ptrsl2003,
  AUTHOR = {Di Paolo, E.},
  YEAR = 2003,
  TITLE = {Evolving spike-timing-dependent plasticity for
		  single-trial learning in robots},
  JOURNAL = {Philosophical Transactions of the Royal Society of London,
		  Series A: Mathematical, Physical and Engineering Sciences},
  VOLUME = 361,
  NUMBER = 1811,
  PAGES = {2299 - 2319}
}

@ARTICLE{doya02,
  AUTHOR = {K. Doya and K. Samejima and K. Katagiri and K. M. Kawato},
  YEAR = 2002,
  TITLE = {Multiple Model Based Reinforcement Learning},
  JOURNAL = {Neural Computation},
  VOLUME = 14,
  PAGES = {1347-1369}
}

@ARTICLE{doya99,
  AUTHOR = {Kenyi Doya},
  YEAR = 1999,
  TITLE = {Reinforcement Learning in Continuous Time and Space},
  JOURNAL = {Neural Computation},
  VOLUME = 12,
  PAGES = {243-269}
}

@BOOK{duda:1972:pcs,
  AUTHOR = {R. O. Duda and P. E. Hart},
  YEAR = 1972,
  TITLE = {Pattern Classification and Scene Analysis},
  PUBLISHER = {Wiley},
  ADDRESS = {New York}
}

@BOOK{ebeling82,
  AUTHOR = {Ebeling, Werner and Faistel, Rainer},
  YEAR = 1982,
  TITLE = {Physik der Selbstorganisation und Evolution},
  PUBLISHER = {Akademie-Verlag},
  ADDRESS = {Berlin}
}

@ARTICLE{esposito:1995:sdt,
  AUTHOR = {F. Esposito and D. Malerba and G. Semeraro},
  YEAR = 1995,
  TITLE = {Simplifying Decision Trees by Pruning and Grafting: New
		  Results},
  JOURNAL = {Lecture Notes in Computer Science},
  VOLUME = 912,
  PAGES = {287--??},
  ISSN = {0302-9743},
  BIBDATE = {Sat May 11 13:45:32 MDT 1996},
  CODEN = {LNCSD9},
  ACKNOWLEDGEMENT = ACK-NHFB
}

@BOOK{everett95,
  AUTHOR = {H. R. Everett},
  YEAR = 1995,
  TITLE = {Sensors for mobile robots},
  PUBLISHER = {A K Peters}
}

@ARTICLE{fajenkaelbling2003,
  AUTHOR = {Brett R. Fajen and William H. Warren and Selim Temizer and
		  Leslie Pack Kaelbling},
  YEAR = 2003,
  TITLE = {A Dynamical Model of Visually-Guided Steering, Obstacle
		  Avoidance, and Route Selection},
  JOURNAL = {Int. J. Comput. Vision},
  VOLUME = 54,
  NUMBER = {1-3},
  PAGES = {13--34},
  PUBLISHER = {Kluwer Academic Publishers},
  ADDRESS = {Hingham, MA, USA},
  ISSN = {0920-5691}
}

@ARTICLE{feldman02,
  AUTHOR = {Daniel E. Feldman},
  YEAR = 2002,
  TITLE = {Synapses, Scaling and Homeostasis in Vivo},
  JOURNAL = {Nature Neuroscience},
  VOLUME = 5,
  PAGES = {712 - 714}
}

@ARTICLE{feldman80,
  AUTHOR = {A. Feldman},
  YEAR = 1980,
  TITLE = {Superposition of Motor Programs, {I}. {R}hytmic Forearm
		  Movements in Man},
  JOURNAL = {Neuroscience},
  VOLUME = 5,
  PAGES = {81-90}
}

@INPROCEEDINGS{floreano:1997:gsrq,
  AUTHOR = {Dario Floreano and Stefano Nolfi},
  EDITOR = {John R. Koza and Kalyanmoy Deb and Marco Dorigo and David
		  B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo},
  MONTH = {13-16 } # JUL,
  YEAR = 1997,
  TITLE = {God Save the Red Queen! Competition in Co-Evolutionary
		  Robotics},
  BOOKTITLE = {Genetic Programming 1997: Proceedings of the Second Annual
		  Conference},
  PUBLISHER = {Morgan Kaufmann},
  ADDRESS = {Stanford University, CA, USA},
  KEYWORDS = {Artifical life and evolutionary robotics}
}

@INPROCEEDINGS{franceschini97,
  AUTHOR = {N. Franceschini and R. Chagneux},
  TITLE = {Repetitive scanning in the fly compound eye},
  BOOKTITLE = {Proceedings G"ottingen Neurobiology Conference}
}

@INPROCEEDINGS{franceschini99,
  AUTHOR = {S. Viollet and N. Franceschini},
  YEAR = 1999,
  TITLE = {Visual servo system based on a biologically-inspired
		  scanning sensor},
  BOOKTITLE = {Sensor Fusion and decentralized Control in Robotic Systems
		  II},
  VOLUME = {SPIE Vol. 3839},
  PAGES = {144--155},
  ADDRESS = {Bellingham, USA}
}

@ARTICLE{fregnac98,
  AUTHOR = {Yves Fr{\'e}gnac},
  YEAR = 1998,
  TITLE = {Homeostasis or Synaptic Plasticity},
  JOURNAL = {Nature},
  VOLUME = 391,
  PAGES = {845-846}
}

@ARTICLE{fritzke94,
  AUTHOR = {Bernd Fritzke},
  YEAR = 1994,
  TITLE = {Growing Cell Structures --- {A} Self-Organizing Network
		  for Unsupervised and Supervised Learning},
  JOURNAL = {Neural Networks},
  VOLUME = 7,
  NUMBER = 9,
  PAGES = {1441--1460},
  ABSTRACT = {Additive unsupervised learning structure. Can be extended
		  with RBFs to get supervised learning, RBFs are positioned
		  automatically. Good results.},
  CLASS = {nn, learning, additive, constructive},
  NOTES = {^^}
}

@BOOK{fugonzalez87,
  AUTHOR = {K. S. Fu and R. C. Gonzalez},
  YEAR = 1987,
  TITLE = {Robotics},
  PUBLISHER = {McGraw--Hill}
}

@ARTICLE{fukico03,
  AUTHOR = {Y. Fukoka and H. Kimura and A.H. Cohen},
  YEAR = 2003,
  TITLE = {Adaptive dynamic walking of a quadruped robot on irregular
		  terrain based on biological concepts},
  JOURNAL = {International Journal of Robotics Research},
  VOLUME = 22,
  NUMBER = {3-4},
  PAGES = {187-202},
  KEY = {FuKiCo03}
}

@ARTICLE{furnkranz97,
  AUTHOR = {Johannes F{\"u}rnkranz},
  YEAR = 1997,
  TITLE = {Pruning Algorithms for Rule Learning},
  JOURNAL = {Machine Learning},
  VOLUME = 27,
  PAGES = 139
}

@BOOK{gardiner85,
  AUTHOR = {C. W. Gardiner},
  YEAR = 1985,
  TITLE = {Handbook of Stochastic Methods},
  PUBLISHER = {Springer}
}

@BOOK{gardiner90,
  AUTHOR = {C. W. Gardiner},
  YEAR = 1990,
  TITLE = {Handbook of Stochastic Methods},
  PUBLISHER = {Springer}
}

@ARTICLE{gell-mann96,
  AUTHOR = {Gell-Mann,M and LLoyd,S},
  YEAR = 1996,
  TITLE = {Information Measures, Effective Complexity and Total
		  Information},
  JOURNAL = {Complexity},
  VOLUME = 2,
  PAGES = {44-52}
}

@BOOK{gershogray92,
  AUTHOR = {A. Gersho and R. M. Gray},
  YEAR = 1992,
  TITLE = {Vector quantization and signal processing},
  PUBLISHER = {Kluwer Academic Publishers},
  ADDRESS = {Boston/Dordrecht/London}
}

@BOOK{gerstner02,
  AUTHOR = {W. Gerstner and W. M. Kistler},
  YEAR = 2002,
  TITLE = {Spiking Neuron Models: Single Neurons, Populations,
		  Plasticity},
  PUBLISHER = {Cambridge University Press},
  ADDRESS = {New York}
}

@BOOK{glasersfeld95,
  AUTHOR = {E. Von Glasersfeld},
  YEAR = {1995},
  TITLE = {Radical Constructivism: A Way of Knowing and Learning.},
  PUBLISHER = {Falmer},
  ADDRESS = {Washington DC}
}

@BOOK{goldberg89,
  AUTHOR = {D. E. Goldberg},
  YEAR = 1989,
  TITLE = {Genetic Algorithms in Search, Optimization, and Machine
		  Learning},
  PUBLISHER = {Addison Wesley}
}

@ARTICLE{golombhertzpanzeri97,
  AUTHOR = {David Golomb and John Hertz and Stefano Panzeri and
		  Alessandro Treves and Barry Richmond},
  YEAR = 1997,
  TITLE = {How Well Can We Estimate the Information Carried in
		  Neuronal Responses from Limited Samples?},
  JOURNAL = {Neural Computation},
  VOLUME = 9,
  NUMBER = 3,
  PAGES = {649--665}
}

@ARTICLE{goswami98,
  AUTHOR = {Goswami, A. and Thuilot, B. and Espiau, B.},
  YEAR = 1998,
  TITLE = {A Study of the Passive Gait of a Compass-Like Biped Robot:
		  Symmetry and Chaos},
  JOURNAL = {International Journal of Robotics Research}
}

@BOOK{haken87,
  AUTHOR = {H. Haken},
  YEAR = 1987,
  TITLE = {Advanced Synergetics},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin}
}

@PHDTHESIS{hamed06,
  AUTHOR = {Naglaa Hamed},
  YEAR = 2006,
  TITLE = {Self-Referential Dynamical Systems and Developmental
		  Robotics},
  NOTE = {In preparation},
  SCHOOL = {University of Leipzig},
  COMMENT = {In preparation}
}

@INPROCEEDINGS{harvey1993,
  AUTHOR = {I.~Harvey and P.~Husbands and D.~T.~Cliff},
  EDITOR = {Stephanie Forrest},
  YEAR = 1993,
  TITLE = {Genetic Convergence in a Species of Evolved Robot Control
		  Architectures},
  BOOKTITLE = {Proceedings of the Fifth International Conference on
		  Genetic Algorithms},
  PAGES = 636,
  NOTE = {Poster version of \cite{Harvey:1993:GCSb}},
  PUBLISHER = {Morgan Kaufmann},
  ADDRESS = {San Mateo, CA, USA}
}

@ARTICLE{harvey2005,
  AUTHOR = {Harvey, I. and Di Paolo, E. and Wood, R. and Quinn, M. and
		  Tuci, E. A.},
  YEAR = 2005,
  TITLE = {Evolutionary Robotics: A new scientific tool for studying
		  cognition},
  JOURNAL = {Artificial Life},
  VOLUME = 11,
  NUMBER = {1-2},
  PAGES = {79-98}
}

@INPROCEEDINGS{harvey:1993:gcsc,
  AUTHOR = {I.~Harvey and P.~Husbands and D.~T.~Cliff},
  EDITOR = {Stephanie Forrest},
  YEAR = 1993,
  TITLE = {Genetic Convergence in a Species of Evolved Robot Control
		  Architectures},
  BOOKTITLE = {Proceedings of the Fifth International Conference on
		  Genetic Algorithms},
  PAGES = 636,
  NOTE = {Poster version of \cite{Harvey:1993:GCSb}},
  PUBLISHER = {Morgan Kaufmann},
  ADDRESS = {San Mateo, CA, USA}
}

@BOOK{haschke01,
  AUTHOR = {Robert Haschke and Jochen J. Steil and Helge Ritter},
  YEAR = 2001,
  TITLE = {Controlling Oscillatory Behaviour of a Two Neuron
		  Recurrent Neural Network Using Inputs},
  SERIES = {Lecture Notes in Computer Science},
  VOLUME = 2130,
  PAGES = 1109,
  PUBLISHER = {Springer}
}

@PHDTHESIS{haschke2003-bid,
  AUTHOR = {Robert Haschke},
  MONTH = {Sep},
  YEAR = 2003,
  TITLE = {Bifurcations in Discrete-Time Neural Networks --
		  Controlling Complex Network Behaviour with Inputs},
  SCHOOL = {Bielefeld University},
  PAPERLOCATION = {
		  http://www.techfak.uni-bielefeld.de/ags/ni/publications/media/Haschke2003-BID.pdf}
}

@ARTICLE{haschkesteil2005-isb,
  AUTHOR = {Robert Haschke and Jochen J. Steil},
  YEAR = 2005,
  TITLE = {Input Space Bifurcation Manifolds of Recurrent Neural
		  Networks},
  JOURNAL = {Neurocomputing},
  VOLUME = {64C},
  PAGES = {25-38},
  HOWPUBLISHED = {\url{
		  http://www.techfak.uni-bielefeld.de/ags/ni/publications/media/HaschkeSteil2005-ISB.pdf}}
}

@INPROCEEDINGS{haschkesteilritter2001-cob,
  AUTHOR = {Robert Haschke and Jochen J. Steil and Helge Ritter},
  YEAR = 2001,
  TITLE = {Controlling Oscillatory Behavior of a Two Neuron Recurrent
		  Neural Network Using Inputs},
  BOOKTITLE = {Proc. of the Int. Conf. on Artificial Neural Networks
		  (ICANN)},
  ADDRESS = {Wien, Austria}
}

@ARTICLE{hau.ea:learning-qualitative:97,
  AUTHOR = {D. T. Hau and E. W. Coiera},
  YEAR = 1997,
  TITLE = {Learning qualitative models of dynamic systems},
  JOURNAL = {Machine Learning},
  VOLUME = 26,
  PAGES = {177--211},
  ADDRESS = {Johns Hopkins Univ, Sch Med, Baltimore, Md, 21205 Hewlett
		  Packard Labs, Bristol Bs12 6Qz, Avon, England},
  ABSTRACT = {The automated construction of dynamic system models is an
		  important application area for ILP. We describe a method
		  that learns qualitative models from time-varying
		  physiological signals. The goal is to understand the
		  complexity of the learning task when faced with numerical
		  data, what signal processing techniques are required, and
		  how this affects learning. The qualitative representation
		  is based on Kuipers' QSIM. The learning algorithm for model
		  construction is based on Coiera's GENMODEL. We show that
		  QSIM models are efficiently PAC learnable from positive
		  examples only, and that GENMODEL is an ILP algorithm for
		  efficiently constructing a QSIM model. We describe both
		  GENMODEL which performs RLGG on qualitative states to learn
		  a QSIM model, and the front-end processing and segmenting
		  stages that transform a signal into a set of qualitative
		  states. Next we describe results of experiments on data
		  from six cardiac bypass patients. Useful models were
		  obtained, representing both normal and abnormal
		  physiological states. Model variation across time and
		  across different levels of temporal abstraction and fault
		  tolerance is explored. The assumption made by many previous
		  workers that the abstraction of examples from data can be
		  separated from the learning task is not supported by this
		  study. Firstly, the effects of noise in the numerical data
		  manifest themselves in the qualitative examples. Secondly,
		  the models learned are directly dependent on the initial
		  qualitative abstraction chosen.},
  KEYWORDS = {SIMULATION, inductive logic programming, qualitative
		  modelling, system identification, PAC learning,
		  physiological modelling, cardiovascular system, data
		  mining, patient monitoring},
  ISSUE = {2-3}
}

@ARTICLE{haya99,
  AUTHOR = {K. Hase and N. Yamazaki},
  YEAR = 1999,
  TITLE = {Computational evolution of human bipedal walking by a
		  neuro-musculo-skeletal model},
  JOURNAL = {Artificial Life and Robotics},
  VOLUME = 3,
  PAGES = {133-138},
  KEY = {HaYa99}
}

@ARTICLE{hockschonergiese2003,
  AUTHOR = {Hock, H S and Sch{\"o}ner, G and Giese, M A},
  YEAR = 2003,
  TITLE = {The dynamical foundations of motion pattern formation:
		  {S}tability, selective adaptation, and perceptual
		  continuity},
  JOURNAL = {Perception \& Psychophysics},
  VOLUME = 65,
  PAGES = {429-457},
  HOWPUBLISHED = {\url{ftp://ftp.neuroinformatik.rub.de/pub/manuscripts/articles/HockSchonerGiese2003.pdf}}
}

@ARTICLE{hofbuh97,
  AUTHOR = {Hofmann and Buhmann},
  YEAR = 1997,
  TITLE = {Correction to ``Pairwise Data Clustering by Deterministic
		  Annealing''},
  JOURNAL = {IEEETPAMI: IEEE Transactions on Pattern Analysis and
		  Machine Intelligence},
  VOLUME = 19
}

@INPROCEEDINGS{hoffmeister90,
  AUTHOR = {F. Hoffmeister and T. B{\"a}ck},
  EDITOR = {H. -P. Schwefel and R. M{\"a}nner},
  MONTH = {1-3 } # OCT,
  YEAR = 1991,
  TITLE = {Genetic algorithms and evolution strategies: similarities
		  and differences},
  BOOKTITLE = {Parallel Problem Solving from Nature - Proceedings of 1st
		  Workshop, PPSN 1},
  SERIES = {Lecture Notes in Computer Science},
  VOLUME = 496,
  PAGES = {455--469},
  PUBLISHER = {Springer-Verlag, Berlin, Germany},
  ADDRESS = {Dortmund, Germany}
}

@BOOK{honerkamp90,
  AUTHOR = {J. Honerkamp},
  YEAR = 1990,
  TITLE = {Stochastische dynamische {S}ysteme},
  PUBLISHER = {VCH Verlagsgesellschaft}
}

@BOOK{hoppensteadt97,
  AUTHOR = {F. C. Hoppensteadt and E. M. Izhikevich},
  YEAR = 1997,
  TITLE = {Weakly Coupled Neural Networks},
  PUBLISHER = {Springer}
}

@BOOK{huelsepasemann02,
  AUTHOR = {Martin H{\"u}lse and Frank Pasemann},
  YEAR = 2002,
  TITLE = {Dynamical Neural Schmitt Trigger for Robot Control},
  SERIES = {Lecture Notes in Computer Science},
  VOLUME = 2415,
  PUBLISHER = {Springer},
  ADDRESS = {Berlin, Heidelberg, New York}
}

@TECHREPORT{husbands93,
  AUTHOR = {P. Husbands and I. Harvey and D. Cliff},
  YEAR = 1993,
  TITLE = {Analysing recurrent dynamical networks evolved for robot
		  control},
  NUMBER = {Cognitive Science Research Paper CSRP265},
  ADDRESS = {Brighton BN1 9QH, England, UK},
  INSTITUTION = {School of Cognitive and Computing Sciences, University of
		  Sussex}
}

@ARTICLE{ieee-nn:galanopoulos+ahalt:1996,
  AUTHOR = {A. S. Galanopoulos and Stanley C. Ahalt},
  MONTH = MAY,
  YEAR = 1996,
  TITLE = {Codeword Distribution for Frequency Sensitive Competitive
		  Learning with One-Dimensional Input Data},
  JOURNAL = {IEEE Transactions on Neural Networks},
  VOLUME = 7,
  NUMBER = 3,
  PAGES = {752--756},
  TYPE = {Brief Paper}
}

@INPROCEEDINGS{iida05,
  AUTHOR = {Iida, F. and Gomez, G. J. and Pfeifer, R.},
  YEAR = 2005,
  TITLE = {Exploiting Body Dynamics for Controlling a Running
		  Quadruped Robot.},
  BOOKTITLE = {International Conference of Advanced Robotics {(ICAR}
		  05)}
}

@BOOK{isaacs,
  AUTHOR = {R. Isaacs},
  YEAR = {},
  TITLE = {Differential Games},
  PUBLISHER = {},
  ADDRESS = {}
}

@BOOK{jacob97,
  AUTHOR = {C. Jacob},
  YEAR = 1997,
  TITLE = {Principia Evolvica - Simulierte Evolution mit
		  Mathematica},
  PUBLISHER = {dpunkt-Verlag},
  ADDRESS = {Heidelberg}
}

@ARTICLE{jacobs93,
  AUTHOR = {R. A. Jacobs and M. I. Jordan},
  YEAR = 1993,
  TITLE = {Learning Piecewise Control Strategies in a Modular
		  Neural-Network Architecture},
  JOURNAL = {IEEE Transactions on Systems, Man, and Cybernetics},
  VOLUME = 23,
  NUMBER = 2,
  PAGES = {337--345},
  ABSTRACT = {The dynamics of nonlinear systems often vary qualitatively
		  over their parameter space. Methodologies for designing
		  piecewise control laws for dynamical systems, such as gain
		  scheduling, are useful because they circumvent the problem
		  of determining a single global model of the plant dynamics.
		  Instead, the dynamics are approximated using local models
		  that vary with the plant's operating point. When a
		  controller is learned instead of designed, analogous issues
		  arise. This article describes a multi-network, or modular,
		  neural network architecture that learns to perform control
		  tasks using a piecewise control strategy. The
		  architecture's networks compete to learn the training
		  patterns. As a result, a plant's parameter space is
		  adaptively partitioned into a number of regions, and a
		  different network learns a control law in each region. This
		  learning process is described in a probabilistic framework
		  and learning algorithms that perform gradient ascent in a
		  log likelihood function are discussed. Simulations show
		  that the modular architecture's performance is superior to
		  that of a single network on a multipayload robot motion
		  control task.}
}

@ARTICLE{jacobsjordannowlanhinton91,
  AUTHOR = {R. A. Jacobs and M. I. Jordan and S. J. Nowlan and G. E.
		  Hinton},
  YEAR = 1991,
  TITLE = {Adaptive Mixtures of Local Experts},
  JOURNAL = {Neural Computation},
  VOLUME = 3,
  NUMBER = 1,
  PAGES = {79--87}
}

@BOOK{jason97,
  AUTHOR = {Jason Kingdon},
  YEAR = 11997,
  TITLE = {Intelligent Systems and Financial Forecasting},
  PUBLISHER = {Springer}
}

@ARTICLE{jordanjacobs1994,
  AUTHOR = {M. I. Jordan and R. A. Jacobs},
  YEAR = 1994,
  TITLE = {Hierarchical Mixtures of Experts and the {EM} Algorithm},
  JOURNAL = {Neural Computation},
  VOLUME = 6,
  NUMBER = 2,
  PAGES = {181--214},
  ABSTRACT = {We present a tree-structured architecture for supervised
		  learning. The statistical model underlying the architecture
		  is a hierarchical mixture model in which both the mixture
		  coefficients and the mixture components are generalized
		  linear models (GLIM's).Learning is treated as a maximum
		  likelihood problem; in particular, we present an
		  Expectation-Maximization (EM) algorithm for adjusting the
		  parameters of the architecture.We also develop an on-line
		  learning algorithm in which the parameters are updated
		  incrementally.Comparative simulation results are presented
		  in the robot dynamics domain.}
}

@ARTICLE{journals/neco/porrfw03,
  AUTHOR = {Bernd Porr and Christian von Ferber and Florentin
		  W{\"o}rg{\"o}tter},
  YEAR = 2003,
  TITLE = {{ISO} Learning Approximates a Solution to the
		  Inverse-Controller Problem in an Unsupervised Behavioral
		  Paradigm},
  JOURNAL = {Neural Computation},
  VOLUME = 15,
  NUMBER = 4,
  PAGES = {865--884},
  BIBDATE = {2004-07-08},
  BIBSOURCE = {DBLP,
		  http://dblp.uni-trier.de/db/journals/neco/neco15.html#PorrFW03},
  HOWPUBLISHED = {\url{http://neco.mitpress.org/cgi/content/abstract/15/4/865}}
}

@ARTICLE{journals/neco/porrw03,
  AUTHOR = {Bernd Porr and Florentin W{\"o}rg{\"o}tter},
  YEAR = 2003,
  TITLE = {Isotropic Sequence Order Learning},
  JOURNAL = {Neural Computation},
  VOLUME = 15,
  NUMBER = 4,
  PAGES = {831--864},
  BIBDATE = {2004-07-08},
  BIBSOURCE = {DBLP,
		  http://dblp.uni-trier.de/db/journals/neco/neco15.html#PorrW03},
  URL = {http://neco.mitpress.org/cgi/content/abstract/15/4/831}
}

@ARTICLE{kampis85,
  AUTHOR = {Kampis,G and Csanyi,V},
  YEAR = 1985,
  TITLE = {Simple Models Do Not Eliminate Complexity from the Real
		  World},
  JOURNAL = {Journal of Theoretical Biology},
  VOLUME = 115,
  PAGES = {467-469}
}

@ARTICLE{kampis87,
  AUTHOR = {Kampis,G and Csanyi,V},
  YEAR = 1987,
  TITLE = {Notes on Order and Complexity},
  JOURNAL = {Journal of Theoretical Biology},
  VOLUME = 124,
  PAGES = {111-121}
}

@INCOLLECTION{kampis88,
  AUTHOR = {Kampis,G},
  EDITOR = {Carvallo,ME},
  YEAR = 1988,
  TITLE = {Information, Computation and Complexity},
  BOOKTITLE = {Nature, Cognition and Systems},
  PAGES = {313-320},
  PUBLISHER = {Kluer},
  ADDRESS = {Dordrecht}
}

@INPROCEEDINGS{kaplano03,
  AUTHOR = {Fr{\'e}d{\'e}ric Kaplan and Pierre-Yves Oudeyer},
  YEAR = 2003,
  TITLE = {Maximizing Learning Progress: An Internal Reward System
		  for Development.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {259-270},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=259},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@BOOK{kauffman02,
  AUTHOR = {Stuart A. Kauffman},
  YEAR = 2002,
  TITLE = {Investigations},
  PUBLISHER = {Oxford University Press}
}

@BOOK{kauffman93,
  AUTHOR = {Stuart A. Kauffman},
  YEAR = 1993,
  TITLE = {The Origins of Order: Self-Organization and Selection in
		  Evolution},
  PUBLISHER = {Oxford University Press}
}

@BOOK{kauffman96,
  AUTHOR = {Stuart A. Kauffman},
  YEAR = 1996,
  TITLE = {At Home in the Universe: The Search for Laws of
		  Self-Organization and Complexity},
  PUBLISHER = {Oxford University Press}
}

@ARTICLE{kim04,
  AUTHOR = {Kim, D.H.},
  YEAR = 2004,
  TITLE = {Self-Organization for Multi-Agent Groups},
  JOURNAL = {International Journal of Control, Automation, and
		  Systems},
  VOLUME = 2,
  PAGES = {333-342}
}

@ARTICLE{kimkim03,
  AUTHOR = {Kim, Dong-Han and Kim, Jong-Hwan},
  YEAR = 2003,
  TITLE = {{A real-time limit-cycle navigation method for fast mobile
		  robots and its application to robot soccer.}},
  JOURNAL = {Robot. Auton. Syst.},
  VOLUME = 42,
  NUMBER = 1,
  PAGES = {17-30},
  ABSTRACT = {{Summary: A mobile robot should be designed to navigate
		  with collision avoidance capability in the real world,
		  flexibly coping with the changing environment. In this
		  paper, a novel limit-cycle navigation method is proposed
		  for a fast mobile robot using the limit-cycle
		  characteristics of a 2nd-order nonlinear function. It can
		  be applied to the robot operating in a dynamically changing
		  environment, such as in a robot soccer system. By adjusting
		  the radius of the motion circle and the direction of
		  obstacle avoidance, the navigation method proposed enables
		  a robot to maneuver smoothly towards any desired
		  destination. Simulations and real experiments using a robot
		  soccer system demonstrate the merits and practical
		  applicability of the proposed method.}},
  KEYWORDS = {{robot soccer; navigation; limit cycle; real-time
		  control}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.9 Robotics}}
}

@INPROCEEDINGS{komosinski00,
  AUTHOR = {M. Komosinski},
  YEAR = 2000,
  TITLE = {The World of Framsticks: Simulation, Evolution,
		  Interaction},
  BOOKTITLE = {Proceedings of 2nd International Conference on Virtual
		  Worlds {(VW2000)}, Paris},
  SERIES = {Lecture Notes in Artificial Intelligence 1834},
  PAGES = {214-224.},
  PUBLISHER = {Springer-Verlag},
  ADDRESS = {Berlin, Heidelberg, New York}
}

@INPROCEEDINGS{komosinski05,
  AUTHOR = {M. Komosinski},
  YEAR = 2005,
  TITLE = {Computer Science in Evolutionary Biology and Robotics:
		  Framsticks Artificial Life},
  HOWPUBLISHED = {\url{http://www.frams.alife.pl/}}
}

@INPROCEEDINGS{koscha00,
  AUTHOR = {S. Kotosaka and S. Schaal},
  YEAR = 2000,
  TITLE = {Synchronized robot drumming by neural oscillators},
  BOOKTITLE = {Proceedings of the International Symposium on Adaptive
		  Motion of Animals and Machines},
  KEY = {KoScha00}
}

@BOOK{koza93,
  AUTHOR = {J. R. Koza},
  YEAR = 1993,
  TITLE = {Genetic Programming: On the Programming of Computers by
		  Means of Natural Selection. 2nd edition},
  PUBLISHER = {MIT Press}
}

@ARTICLE{koza:1991:cfd,
  AUTHOR = {J. R. Koza},
  YEAR = 1991,
  TITLE = {Concept Formation and Decision Tree Induction Using the
		  Genetic Programming Paradigm},
  JOURNAL = {Lecture Notes in Computer Science},
  VOLUME = 496,
  PAGES = {124--??}
}

@ARTICLE{kroghsol97,
  AUTHOR = {A. Krogh and P. Sollich},
  YEAR = 1997,
  TITLE = {Statistical Mechanics of Ensemble Learning},
  JOURNAL = {Physical Review},
  VOLUME = {E 55},
  PAGES = 811
}

@INPROCEEDINGS{kuniyoshiyotony03,
  AUTHOR = {Yasuo Kuniyoshi and Yasuaki Yorozu and Yoshiyuki Ohmura
		  and Koji Terada and Takuya Otani and Akihiko Nagakubo and
		  Tomoyuki Yamamoto},
  YEAR = 2003,
  TITLE = {From Humanoid Embodiment to Theory of Mind.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {202-218},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin, Heidelberg, New York},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=202},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{labusch02:_sensor_evolut_homeok_system,
  AUTHOR = {Kai Labusch and Daniel Polani},
  YEAR = 2002,
  TITLE = {Sensor Evolution in a Homeokinetic System},
  PAGES = {199-208},
  PUBLISHER = {Infix Aka},
  CROSSREF = {polani02:_fifth_german_works_artif_life_march_german}
}

@ARTICLE{langton90,
  AUTHOR = {C. G. Langton},
  YEAR = 1990,
  TITLE = {Computation at the edge of chaos},
  JOURNAL = {Physica D},
  VOLUME = 42
}

@INPROCEEDINGS{langton92,
  AUTHOR = {Christopher Langton},
  EDITOR = {Christopher G. Langton and Charles Taylor and J. Doyne
		  Farmer and Steen Rasmussen},
  MONTH = FEB,
  YEAR = 1992,
  TITLE = {Life at the Edge of Chaos},
  BOOKTITLE = {Proceedings of the Workshop on Artificial Life ({ALIFE}
		  '90)},
  SERIES = {Santa Fe Institute Studies in the Sciences of Complexity},
  VOLUME = 5,
  PAGES = {41--92},
  PUBLISHER = {Addison-Wesley},
  ADDRESS = {Redwood City, CA, USA},
  ISBN = {0-201-52570-4}
}

@INPROCEEDINGS{larizakhor92,
  AUTHOR = {Francesco Lari and Avideh Zakhor},
  YEAR = 1992,
  TITLE = {Automatic Classification of Active Sonar Data Using
		  Time-Frequency Transforms},
  BOOKTITLE = {Time-Frequency and Time-Scale Analysis},
  PAGES = {21--24},
  ADDRESS = {Victoria, B.C., Canada},
  ORGANIZATION = {IEEE Signal Processing Society},
  ABSTRACT = {Automatic classification of active sonar signals using the
		  Wigner-Ville transform (WVT), the wavelet transform (WT)
		  and the scalogram is addressed. Features are extracted by
		  integrating over regions in the time-frequency (TF)
		  distribution, and are classified by a decision tree.
		  Experimental results show classification and detection
		  rates of up to 92% at -4 dB of SNR. The WT outperforms the
		  WVT and the scalogram, particularly at high noise levels.
		  This can be partially attributed to the absence of cross
		  terms in the WT.}
}

@MISC{leen94,
  AUTHOR = {Todd K. Leen},
  TITLE = {Stochastic {M}anhattan Learning: An Exact Time-Evolution
		  Operator for the Ensemble Dynamics},
  HOWPUBLISHED = {
		  ftp://speech.cse.ogi.edu/pub/neural/papers/LeenMoody96.StochMan.ps.Z}
}

@INCOLLECTION{legensteinmaass:05,
  AUTHOR = {R. Legenstein and W. Maass},
  EDITOR = {S. Haykin and J. C. Principe and T.J. Sejnowski and J.G.
		  McWhirter},
  YEAR = 2005,
  TITLE = {What makes a dynamical system computationally powerful?},
  BOOKTITLE = {New Directions in Statistical Signal Processing: From
		  Systems to Brain},
  NOTE = {to appear},
  PUBLISHER = {MIT Press}
}

@ARTICLE{legensteinmaass:05a,
  AUTHOR = {R. A. Legenstein and W. Maass},
  YEAR = 2005,
  TITLE = {Edge of Chaos and Prediction of Computational Power for
		  Neural Microcircuit Models},
  JOURNAL = {submitted for publication},
  ABSTRACT = {What makes a neural microcircuit computationally powerful?
		  Or more precisely, which measurable quantities could
		  explain why one microcircuit $C$ is better suited for a
		  particular family of computational tasks than another
		  microcircuit $C'$? One potential answer comes from results
		  on cellular automata and random Boolean networks, where
		  some evidence was provided that their computational power
		  for offline computations is largest at the edge of chaos,
		  i.e. at the transition boundary between order and chaos. We
		  analyse in this article the significance of the edge of
		  chaos for real time computations in neural microcircuit
		  models consisting of spiking neurons and dynamic synapses.
		  We find that the edge of chaos predicts quite well those
		  values of circuit parameters that yield maximal
		  computational power. But obviously it makes no prediction
		  of their computational power for other parameter values.
		  Therefore, we propose a new method for predicting the
		  computational power of neural microcircuit models. The new
		  measure estimates directly the kernel property and the
		  generalization capability of a neural microcircuit. We
		  validate the proposed measure by comparing its prediction
		  with direct evaluations of the computational performance of
		  various neural microcircuit models. This procedure is
		  applied first to microcircuit models that differ with
		  regard to the spatial range of synaptic connections and
		  their strength, and then to microcircuit models that differ
		  with regard to the level of background input currents, the
		  conductance, and the level of noise on the membrane
		  potential of neurons. In the latter case the proposed
		  method allows us to quantify differences in the
		  computational power and generalization capability of neural
		  circuits in different dynamic regimes (UP- and DOWN-states)
		  that have been demonstrated through intracellular
		  recordings in vivo.}
}

@MISC{lerman04,
  AUTHOR = {Kristina Lerman and Alcherio Martinoli and Aram Galstyan},
  YEAR = 2004,
  TITLE = {A Review of Probabilistic Macroscopic Models for Swarm
		  Robotic Systems},
  HOWPUBLISHED = {Self-organization of Adaptive Behavior04, Santa Monica,
		  CA}
}

@MISC{lermangalstyan04,
  AUTHOR = {Kristina Lerman and Aram Galstyan},
  YEAR = 2004,
  TITLE = {Automatically Modeling Group Behavior of Simple Agents},
  HOWPUBLISHED = {Agent Modeling Workshop, AAMAS-04, New York, NY.}
}

@BOOK{libet04,
  AUTHOR = {Benjamin Libet},
  YEAR = 2004,
  TITLE = {Mind Time : The Temporal Factor in Consciousness},
  PUBLISHER = {Harvard University Press},
  COMMENT = {Over a long and distinguished career Benjamin Libet has
		  conducted experiments that have helped us see, in clear and
		  concrete ways, how the brain produces conscious awareness.
		  For the first time, Libet gives his own account of these
		  experiments and their importance for our understanding of
		  consciousness. Most notably, Libet's experiments reveal a
		  substantial delay--the "mind time" of the title--before any
		  awareness affects how we view our mental activities. If all
		  conscious awarenesses are preceded by unconscious
		  processes, as Libet observes, we are forced to conclude
		  that unconscious processes initiate our conscious
		  experiences. Freely voluntary acts are found to be
		  initiated unconsciously before an awareness of wanting to
		  act--a discovery with profound ramifications for our
		  understanding of free will. How do the physical activities
		  of billions of cerebral nerve cells give rise to an
		  integrated conscious subjective awareness? How can the
		  subjective mind affect or control voluntary actions? Libet
		  considers these questions, as well as the implications of
		  his discoveries for the nature of the soul, the identity of
		  the person, and the relation of the non-physical subjective
		  mind to the physical brain that produces it. Rendered in
		  clear, accessible language, Libet's experiments and
		  theories will allow interested amateurs and experts alike
		  to share the experience of the extraordinary discoveries
		  made in the practical study of consciousness. This book is
		  strikingly different from most of the other books on
		  consciousness in one key respect: it focuses on empirical
		  discoveries, not speculation or argument. --From the
		  Foreword by Stephen Kosslyn}
}

@ARTICLE{libet85,
  AUTHOR = {Benjamin Libet},
  YEAR = 1985,
  TITLE = {Unconscious cerebral initiative and the role of conscious
		  will in voluntary action},
  JOURNAL = {Behavioral and Brain Sciences},
  VOLUME = 8,
  PAGES = {529--566}
}

@ARTICLE{libet89,
  AUTHOR = {Benjamin Libet},
  YEAR = 1989,
  TITLE = {The timing of a subjective experience},
  JOURNAL = {Behavioral and Brain Sciences},
  VOLUME = 12,
  PAGES = {183-185}
}

@BOOK{libet99,
  AUTHOR = {Benjamin Libet and Anthony Freeman and J. K. B. Sutherland},
  YEAR = 1999,
  TITLE = {The Volitional Brain - Towards a Neuroscience of Free
		  Will},
  PUBLISHER = {Imprint Academic},
  COMMENT = {Many scientists and philosophers claim that everything in
		  the physical world (including all our own actions) is
		  predetermined. On this view, no one is responsible for
		  anything they do, so punishment is an inappropriate
		  response to crime. The Volitional Brain looks at the
		  evidence from science, psychology, and philosophy and
		  debates these issues from the standpoint of both "sceptics"
		  and "libertarians." Principal editor Benjamin Libet showed
		  in 1985 that the brain unconsciously prepares to act a
		  measurable length of time before a person consciously
		  decides to act. In this book he explains why this amazing
		  evidence has not destroyed his belief in free will. Other
		  contributors include: David Hodgson, a high court judge
		  with strong views on the need for free will in the justice
		  system; Jaron Lanier, a professional recording artist as
		  well as computer scientist who coined the term "virtual
		  reality," who writes here about the strangeness of time in
		  relation to freedom; and Anthony Freeman (the Sussex vicar
		  sacked when he decided to publish God In Us, SCM Press,
		  1993), who claims he never made a conscious decision in his
		  life! There are also plenty of scientific articles
		  reporting and explaining both the workings of the brain and
		  the latest views from the physics laboratory.}
}

@ARTICLE{libugray80,
  AUTHOR = {Y. Linde and A. Buzo and R. M. Gray},
  YEAR = 1980,
  TITLE = {An algorithm for vector quantizer design},
  JOURNAL = {IEEE Transactions Comm.},
  VOLUME = 28,
  PAGES = {84-95}
}

@ARTICLE{liebman92,
  AUTHOR = {M.~J. Liebmann and T.~F. Edgar and L.~S. Ladson},
  YEAR = 1992,
  TITLE = {Efficient data reconciliation and estimation for dynamic
		  processes using nonlinear dynamic programming techniques},
  JOURNAL = {Computers in Chemical Engineering},
  VOLUME = 16,
  NUMBER = {10/11},
  PAGES = {961 - 985}
}

@PHDTHESIS{liebscher05,
  AUTHOR = {Ren{\'e} Liebscher},
  YEAR = 2005,
  TITLE = {Echtzeitf\"ahige Lernverfahren F\"ur Kooperierende Roboter},
  SCHOOL = {University Leipzig, Institute of Computer Science}
}

@TECHREPORT{limlohshih97,
  AUTHOR = {T.-S. Lim and W.-Y. Loh and Y.-S. Shih},
  MONTH = JUN # { 30},
  YEAR = 1997,
  TITLE = {An Empirical Comparison of Decision Trees and Other
		  Classification Methods},
  NUMBER = 979,
  ADDRESS = {Madison, WI},
  INSTITUTION = {Department of Statistics, University of
		  Wisconsin-Madison},
  URL = {ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.6/compare.ps.gz}
}

@ARTICLE{lindenmayer68i,
  AUTHOR = {Aristid Lindenmayer},
  YEAR = 1968,
  TITLE = {Mathematical models for cellular interactions in
		  Development, {I}. Filaments with One-sided Inputs},
  JOURNAL = {Journal of Theoretical Biology},
  VOLUME = 18,
  PAGES = {280-299}
}

@ARTICLE{lindenmayer68ii,
  AUTHOR = {Aristid Lindenmayer},
  YEAR = 1968,
  TITLE = {Mathematical models for cellular interactions in
		  Development, {II}. Simple and Branching Filaments with
		  Two-sided Inputs},
  JOURNAL = {Journal of Theoretical Biology},
  VOLUME = 18,
  PAGES = {300-315}
}

@ARTICLE{lindenmayer71,
  AUTHOR = {Aristid Lindenmayer},
  YEAR = 1971,
  TITLE = {Developmental Systems without Cellular Interactions, their
		  Languages and Grammars},
  JOURNAL = {Journal of Theoretical Biology},
  VOLUME = 30,
  PAGES = {455-484}
}

@ARTICLE{lindner04,
  AUTHOR = {B. Lindner and J. Garcia-Ojalvo and A. Neiman and L.
		  Schimansky-Geier},
  YEAR = 2004,
  TITLE = {Effects of Noise in Excitable Systems},
  JOURNAL = {Phys. Report},
  VOLUME = {392,},
  PAGES = {321-424}
}

@MISC{luc96origins,
  AUTHOR = {S. Luc},
  YEAR = 1996,
  TITLE = {The origins of intelligence},
  URL = {citeseer.nj.nec.com/62863.html},
  TEXT = {Steels, Luc, The origins of intelligence, Proceedings of
		  the Carlo Erba Foundation, Meeting on Artificial Life.
		  Fondazione Carlo Erba. Milano, March 12, 1996.}
}

@ARTICLE{lung_04,
  AUTHOR = {Lungarella, Max and Metta, Giorgio and Pfeifer, Rolf and
		  Sandini, Giulio},
  YEAR = 2004,
  TITLE = {Developmental robotics: a survey},
  JOURNAL = {Connection Science},
  VOLUME = 0,
  NUMBER = 0,
  PAGES = {1-40},
  ABSTRACT = {Developmental robotics is an emerging field located at the
		  intersection of robotics, cognitive science and
		  developmental sciences. This paper elucidates the main
		  reasons and key motivations behind the convergence of
		  fields with seemingly disparate interests, and shows why
		  developmental robotics might prove to be beneficial for all
		  fields involved. The methodology advocated is synthetic and
		  two-pronged: on the one hand, it employs robots to
		  instantiate models originating from developmental sciences;
		  on the other hand, it aims to develop better robotic
		  systems by exploiting insights gained from studies on
		  ontogenetic development. This paper gives a survey of the
		  relevant research issues and points to some future research
		  directions.},
  KEYWORDS = {developmental robotics, artificial intelligence},
  COMMENTS = {Review des recherches men\'ees \`a ce jour en robotique
		  d\'eveloppementale et \'epig\'en\'etique. Seulement
		  int\'eressant pour les r\'ef\'erences},
  RATING = {D},
  PDF = {http://www.liralab.it/papers/papers/Papers_04/conn_science04.pdf},
  PDF = {files/lungalella04.pdf}
}

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}

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  ABSTRACT = {Feasibility of applying evolutionary methods to
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		  challenges, unanswered problems, promising directions},
  KEYWORDS = {genetic algorithms, genetic programming, robots}
}

@ARTICLE{mataric:1995:cecpr,
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  VOLUME = 19,
  NUMBER = 1,
  PAGES = {67--83},
  ABSTRACT = {Feasibility of applying evolutionary methods to
		  automatically generating controllers for physical mobile
		  robots. Overview state of the art, main approaches, key
		  challenges, unanswered problems, promising directions},
  KEYWORDS = {genetic algorithms, genetic programming, robots}
}

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  ACKNOWLEDGEMENT = ACK-NHFB
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  YEAR = {},
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  JOURNAL = {to be published},
  VOLUME = {},
  PAGES = {}
}

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  ADDRESS = {Berlin}
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@INCOLLECTION{muesmoraeschkohvap98,
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  ADDRESS = {Cambridge, MA}
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		  learning algorithm in the design of a classifier for a new
		  approach to the mapping of a mobile robot's local
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		  echoes of an ultrasonic array mounted on the robot to
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		  contours are classified into a finite number of two
		  dimensional shapes to form a primitive map which is to be
		  used for navigation. The nature of the problem, noise and
		  the practical timing constraints, distinguishes it from
		  those typically used in machine learning applications and
		  highlights some of the advantages of decision tree learning
		  in robotic applications.},
  URL = {
		  ftp://ftp.cs.helsinki.fi/pub/Reports/by_Project/PMDM/Learning_Decision_Trees_for_Mapping_the_Local_Environment_in_Mobile_Robot_Navigation.ps.gz}
}

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@MISC{oai:citeseerpsu:100230,
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  MONTH = SEP # {~25},
  YEAR = 1995,
  TITLE = {Incremental Learning of Local Linear Mappings},
  ANNOTE = {Bernd Fritzke (Institut f{"}ur Neuroinformatik,
		  Ruhr-Universit{"}at Bochum, Germany);},
  ABSTRACT = {A new incremental network model for supervised learning is
		  proposed. The model builds up a structure of units each of
		  which has an associated local linear mapping (LLM). Error
		  information obtained during training is used to determine
		  where to insert new units whose LLMs are interpolated from
		  their neighbors. Simulation results for several
		  classification tasks indicate fast convergence as well as
		  good generalization. The ability of the model to also
		  perform function approximation is demonstrated by an
		  example. 1 Introduction Local (or piece-wise) linear
		  mappings (LLMs) are an economic means of describing a
		  {"}wellbehaved {"} function f : R n ! R m . The principle
		  is to approximate the function (which may be given by a
		  number of input/output samples ({}; i) 2 R n Theta R m )
		  with a set of linear mappings each of which is constrained
		  to a local region of the input space R n . LLM-based
		  methods have been used earlier to learn the inverse
		  kinematics of robot arms [7], for classificat...},
  LANGUAGE = {en},
  BIBSOURCE = {OAI-PMH server at cs1.ist.psu.edu},
  OAI = {oai:CiteSeerPSU:100230},
  RIGHTS = {unrestricted},
  URL = {http://citeseer.ist.psu.edu/100230.html;
		  http://www.dai.ed.ac.uk/groups/evalg/Local_Copies_of_Papers/Fritzke.ICANN95.Incremental_Learning_of_Local_Linear_Mappings.ps.gz}
}

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  NUMBER = 3,
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		  and other sensorimotor disruptions},
  TEXT = {Di Paolo, E.A., 2000. Homeostatic adaptation to inversion
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		  From Animals to Animats 6. Proceedings of the VI
		  International Conference on Simulation of Adaptive
		  Behavior, (J-A Meyer, A. Berthoz, D. Floreano, H.L.
		  Roitblat, and S.W. Wilson eds.), Cambridge, MA: MIT Press,
		  pp. 440-449.}
}

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  PAGES = {369-76}
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  YEAR = 2002,
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  ADDRESS = {Berlin, Heidelberg, New York}
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  YEAR = 2002,
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  SERIES = {Lecture Notes in Computer Science},
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  PUBLISHER = {Springer}
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  TITLE = {Understanding Intelligence},
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  PAGES = {1-26},
  PUBLISHER = {Bradford Books, MIT Press},
  ADDRESS = {Cambridge, MA},
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  TITLE = {Embodied Artificial Intelligence, International Seminar,
		  Dagstuhl Castle, Germany, July 7-11, 2003, Revised Papers},
  BOOKTITLE = {Embodied Artificial Intelligence},
  SERIES = {Lecture Notes in Computer Science},
  VOLUME = 3139,
  PUBLISHER = {Springer}
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@BOOK{pfeifferschei99,
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  TITLE = {Understanding Intelligence},
  PUBLISHER = {MIT Press,Cambridge, MA}
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  PAGES = {2029-49}
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  EDITOR = {Daniel Polani and Jan Kim and Thomas Martinetz},
  YEAR = 2002,
  TITLE = {Fifth German Workshop on Artificial Life, March 18-20,
		  2002, L{\"u}beck, Germany},
  BOOKTITLE = {Fifth German Workshop on Artificial Life, March 18-20,
		  2002, L{\"u}beck, Germany},
  PUBLISHER = {IOS Press Infix, Aka},
  ANNOTE = {ISBN 3-89838-030-0 (Aka), ISBN 1-58603-242-9 (IOS Press)}
}

@PROCEEDINGS{polani02:_fifth_german_works_artif_life_march_german,
  EDITOR = {Daniel Polani and Jan Kim and Thomas Martinetz},
  YEAR = 2002,
  TITLE = {Fifth German Workshop on Artificial Life, March 18-20,
		  2002, L{\"u}beck, Germany},
  BOOKTITLE = {Fifth German Workshop on Artificial Life, March 18-20,
		  2002, L{\"u}beck, Germany},
  PUBLISHER = {IOS Press Infix, Aka},
  ANNOTE = {ISBN 3-89838-030-0 (Aka), ISBN 1-58603-242-9 (IOS Press)}
}

@MISC{popp04,
  AUTHOR = {Julius Popp},
  YEAR = 2004,
  TITLE = {Sphericalrobots},
  HOWPUBLISHED = {\url{http://www.sphericalrobots.com}}
}

@BOOK{port95,
  EDITOR = {Robert F. Port and Timothy Van Gelder},
  YEAR = 1995,
  TITLE = {Mind as Motion},
  PUBLISHER = {The MIT Press},
  ADDRESS = {Cambridge, MA}
}

@INPROCEEDINGS{pr94b,
  AUTHOR = {Pratt, V.R.},
  YEAR = 1994,
  TITLE = {Chu Spaces: Automata with Quantum Aspects},
  BOOKTITLE = {Proc. Workshop on Physics and Computation (PhysComp'94)},
  PAGES = {186-195},
  PUBLISHER = {{IEEE}},
  ADDRESS = {Dallas}
}

@BOOK{prusinkiewicz89,
  AUTHOR = {Przemyslaw Prusinkiewicz and James S. Hanan},
  YEAR = 1989,
  TITLE = {Lindenmayer Systems, Fractals, and Plants},
  PUBLISHER = {Springer},
  ADDRESS = {New York, NY}
}

@ARTICLE{quinlan+cameron-jones94,
  AUTHOR = {J. R. Quinlan and R. M. Cameron-Jones},
  YEAR = 1994,
  TITLE = {Efficient top-down induction of logic programs},
  JOURNAL = {SIGART Bulletin},
  VOLUME = 5,
  NUMBER = 1,
  PAGES = {33--42}
}

@INBOOK{quinlan83,
  AUTHOR = {J. R. QUINLAN},
  YEAR = 1983,
  TITLE = {Inductive inference as a tool for the construction of
		  efficient classification programs},
  BOOKTITLE = {Machine Learning: an Artificial Intelligence Approach},
  PUBLISHER = {Tioga},
  ADDRESS = {Palo Alto, CA},
  KEY = {quinlan}
}

@BOOK{quinlan91,
  AUTHOR = {P. Quinlan},
  YEAR = 1991,
  TITLE = {Connectionism and Psychology},
  PUBLISHER = {Harvester Wheatsheaf},
  ADDRESS = {New York}
}

@BOOK{quinlan92,
  AUTHOR = {J. R. Quinlan},
  YEAR = 1992,
  TITLE = {{C4}.5: Programs for Machine Learning.},
  PUBLISHER = {Morgan Kaufmann},
  ABSTRACT = {Book only: ISBN 1-55860-238-0 \$ 44.95 U.S. \$49.45
		  International Book and Software: ISBN 1-55860-240-2 \$69.95
		  U.S. \$76.00 International Software only: ISBN
		  1-55860-239-9 \$34.95 U.S. \$38.45 International 1
		  Introduction 2 Constructing Decision Trees 3 Unknown
		  Attribute Values 4 Pruning Decision Trees 5 From Trees to
		  Rules 6 Windowing 7 Grouping Attribute Values 8 Interacting
		  with Classification Models 9 Guide to Using the System 10
		  Limitations 11 Desirable Additions},
  KEYWORDS = {book, decision trees, C4.5, ID3, inductive inference, II,
		  rule rules}
}

@INPROCEEDINGS{quinlan93,
  AUTHOR = {J. R. Quinlan},
  MONTH = JUL,
  YEAR = 1993,
  TITLE = {Combining instance-based and model-based learning},
  BOOKTITLE = {Proceedings of the Tenth International Conference on
		  Machine Learning},
  PAGES = {236--243},
  PUBLISHER = {Morgan Kaufmann},
  ADDRESS = {Amherst, Massachusetts}
}

@ARTICLE{quinlan96,
  AUTHOR = {J. R. Quinlan},
  YEAR = 1996,
  TITLE = {Improved {U}se of {C}ontinuous {A}ttributes in {C4.5}},
  JOURNAL = {Journal of Artificial Intelligence},
  VOLUME = 4,
  PAGES = {77--90}
}

@ARTICLE{quinlan:1986,
  AUTHOR = {J. R. Quinlan},
  YEAR = 1986,
  TITLE = {Induction of decision trees},
  JOURNAL = {Machine Learning},
  VOLUME = 1,
  PAGES = {81--106}
}

@BOOK{rechenberg73,
  AUTHOR = {I. Rechenberg},
  YEAR = 1973,
  TITLE = {Evolutionsstrategie: Optimierung technischer Systeme nach
		  Prinzipien der biologischen Evolution},
  PUBLISHER = {Frommann-Holzboog},
  ADDRESS = {Stuttgart}
}

@BOOK{rechenberg94,
  AUTHOR = {I. Rechenberg},
  YEAR = 1994,
  TITLE = {Evolutionsstrategie'94},
  PUBLISHER = {Frommann-Holzboog},
  ADDRESS = {Stuttgart}
}

@BOOK{rescorla80,
  AUTHOR = {R. A. Rescorla},
  YEAR = 1980,
  TITLE = {Pavlovian Second-Order Conditioning: Studies in
		  Associative Learning},
  PUBLISHER = {Erlbaum/Wiley}
}

@INPROCEEDINGS{rescowagner72,
  AUTHOR = {R. A. Rescorla and A. R. Wagner},
  EDITOR = {A. H. Black and W. F. Prokasy},
  YEAR = 1972,
  TITLE = {A theory of Pavlovian conditioning: variations in the
		  effectiveness of reinforcement and nonreinforcement},
  BOOKTITLE = {Classical Conditioning II: Current Research and Theory},
  PAGES = {64 - 99},
  PUBLISHER = {Appleton-Century-Crofts}
}

@ARTICLE{righetti05c,
  AUTHOR = {Righetti, L. and Buchli, J. and Ijspeert, A.J.},
  YEAR = {},
  TITLE = {Dynamic Hebbian learning in adaptive frequency
		  oscillators},
  JOURNAL = {Physica D},
  NOTE = {In Press},
  TYPE = {article},
  PDF = {http://birg2.epfl.ch/publications/fulltext/righetti05c.pdf}
}

@INPROCEEDINGS{righetti06,
  AUTHOR = {Righetti, L. and Ijspeert A.J.},
  YEAR = 2006,
  TITLE = {Programmable Central Pattern Generators: an application to
		  biped locomotion control},
  BOOKTITLE = {Proceedings of the 2006 IEEE International Conference on
		  Robotics and Automation},
  NOTE = {In press},
  TYPE = {conference},
  PDF = {\url{http://birg2.epfl.ch/publications/fulltext/righetti06.pdf}}
}

@BOOK{risken89,
  AUTHOR = {H. Risken},
  YEAR = 1989,
  TITLE = {The {F}okker-{P}lanck equation},
  PUBLISHER = {Springer}
}

@TECHREPORT{rose-et-al-90b,
  AUTHOR = {K. Rose and E. Gurewitz and G.~C. Fox},
  YEAR = 1990,
  TITLE = {Statistical Mechanics and Phase Transitions in
		  Clustering},
  NUMBER = {CFP-895},
  INSTITUTION = {California Institute of Technology}
}

@BOOK{russellnorvig95,
  AUTHOR = {Stuart Russell and Peter Norvig},
  YEAR = 1995,
  TITLE = {Artifical Intelligence: {A} Modern Approach,},
  PUBLISHER = {Prentice-Hall, Englewood Cliffs, NJ, ISBN 0-13-103805-2,
		  912 pp., 1995},
  INSTITUTION = {UC-Berk/Sun Microsystems Labs},
  ANNOTE = {This massive tome definitely deserves the {"}modern{"}
		  rubric that it has given itself; its approach deals
		  definitively with agent architectures for AI, and the
		  writing is also well-informed and up-to-date, but informal.
		  e.g. the [Elkan 93-94] debate on Fuzzy logic is summarized
		  in one page (463), concluding: {"}As one might expect, the
		  debate continues.{"} --QUOTE--(p.750): For a specific task,
		  one does not need to recover all the information that in
		  principle can be recovered from an image.{"} - w.r.t. a
		  vehicular driving task. The objectives of the driver here
		  are: to move at a reasonable speed v0, to maintain the
		  vehicle in its lane laterally (dL=dR), to ensure safe
		  distance d2 from vehicle ahead, and monitor vehicles in
		  neighbouring lanes at distances d1 and d3, and be prepared
		  if they change lanes. The vision chapter was better in the
		  cotemporaneous Dean/Aloimonos/Allen book, but the NLP
		  section here is stronger. There is also a chapter on
		  robotics (by Canny) focusing on path planning, that also
		  introduces behavioural (situated) robots. --QUOTE--: {"}the
		  principal drawback of the classical view is that explicit
		  reasoning about the effect of low-level actions is too
		  expensive... Researchers working on situated automata
		  [eliminate] explicit deliberation from their robot designs
		  altogether.{}},
  KEYWORDS = {AI AGENT OVERVIEW VISION KNOWLEDGE-REP REACTIVE}
}

@ARTICLE{salenius03,
  AUTHOR = {S. Salenius and R. Hari},
  YEAR = 2003,
  TITLE = {Synchronous Cortical Oscillatory Activity During Motor
		  Control},
  JOURNAL = {Current Opinion in Neurobiology},
  VOLUME = 13,
  PAGES = 678
}

@BOOK{salton89,
  AUTHOR = {G. Salton},
  YEAR = 1989,
  TITLE = {Automatic Text Processing},
  PUBLISHER = {Addison Wesley},
  ADDRESS = {Massachusetts}
}

@BOOK{sayood96,
  AUTHOR = {K. Sayood},
  YEAR = 1996,
  TITLE = {Introduction to data compression},
  PUBLISHER = {Morgan Kaufmann},
  ADDRESS = {San Francisco}
}

@CONFERENCE{schaal00,
  AUTHOR = {S. Schaal and S. Kotosaka},
  YEAR = 2000,
  TITLE = {Synchronized Robot Drumming by Neural Oscillators},
  BOOKTITLE = {The International Symposium on Adaptive Motion of Animals
		  and Machines},
  ADDRESS = {Montreal}
}

@INPROCEEDINGS{schaal03,
  AUTHOR = {Peters, J. and Vijayakumar, S. and Schaal, S.},
  YEAR = 2003,
  TITLE = {Reinforcement Learning for Humanoid Robotics},
  BOOKTITLE = {Humanoids2003, Third {IEEE}-{RAS} International Conference
		  on Humanoid Robots.}
}

@INPROCEEDINGS{schaffer92,
  AUTHOR = {C. Schaffer},
  EDITOR = {Y. Kodratoff},
  MONTH = MAR,
  YEAR = 1991,
  TITLE = {When Does Overfitting Decrease Prediction Accuracy in
		  Induced Decision Trees and Rule Sets?},
  BOOKTITLE = {Proceedings of the European Working Session on Learning :
		  Machine Learning ({EWSL}-91)},
  SERIES = {LNAI},
  VOLUME = 482,
  PAGES = {192--205},
  PUBLISHER = {Springer Verlag},
  ADDRESS = {Porto, Portugal},
  ISBN = {3-540-53816-X}
}

@INPROCEEDINGS{schlageimer02,
  AUTHOR = {H. Bowman and A. Aron and F. Schlaghecken and M. Eimer},
  EDITOR = {John A. Bullinaria & Will Lowe},
  MONTH = {September},
  YEAR = 2002,
  TITLE = {Neural Network Modelling of Inhibition in Visuo-Motor
		  Control},
  BOOKTITLE = {Proceedings of the Seventh Neural Computation and
		  Psychology Workshop: Connectionist Models of Cognition and
		  Perception},
  PAGES = {209-222},
  PUBLISHER = {World Scientific},
  URL = {http://www.cs.ukc.ac.uk/pubs/2002/1342}
}

@ARTICLE{schlierkmuehl96,
  AUTHOR = {D. Schlierkamp-Voosen and H. M{\"u}hlenbein},
  YEAR = 1994,
  TITLE = {Strategy Adaptation by Competing Subpopulations},
  JOURNAL = {Lecture Notes in Computer Science},
  VOLUME = 866,
  PAGES = {199--??},
  ISSN = {0302-9743},
  BIBDATE = {Mon May 13 11:52:14 MDT 1996},
  CODEN = {LNCSD9},
  ACKNOWLEDGEMENT = ACK-NHFB
}

@ARTICLE{schoener95,
  AUTHOR = {G. Sch{\"o}ner, M. Dose and C. Engels},
  YEAR = 1995,
  TITLE = {Dynamics of Behavior: Theory and Applications for
		  Autonomous Robot Architectures.},
  JOURNAL = {Robotics and Autonomous Systems,},
  VOLUME = 16,
  PAGES = {213-245.}
}

@BOOK{schwefel90,
  AUTHOR = {H. P. Schwefel},
  YEAR = 1990,
  TITLE = {Parallel problem solving from nature},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin [u.a.]}
}

@BOOK{schwefel95,
  AUTHOR = {H.-P. Schwefel},
  YEAR = 1995,
  TITLE = {Evolution and Optimum Seeking},
  PUBLISHER = {Wiley},
  ADDRESS = {New York}
}

@BOOK{shavlikdietterich90,
  EDITOR = {J. W. Shavlik and T. G. Dietterich},
  YEAR = 1990,
  TITLE = {Readings in Machine Learning},
  PUBLISHER = {Morgan Kaufmann}
}

@INPROCEEDINGS{siegel92,
  AUTHOR = {E. V. Siegel and K. R. McKeown},
  MONTH = JUL,
  YEAR = 1994,
  TITLE = {Emergent linguistic rules from inducing decision trees:
		  disambiguating discourse clue words},
  BOOKTITLE = {Proceedings of the Twelfth National Conference on
		  Artificial Intelligence},
  PUBLISHER = {AAAI Press},
  ADDRESS = {Menlo Park, CA, USA},
  KEY = {Siegel},
  KEYWORDS = {genetic algorithms, genetic programming},
  NOTES = {report on the same work in which decision tree choice
		  point feature sets are bitstrings, but overall tree is a
		  tree. Subtree swapping and onepoint bitstring crossovers
		  are both used. See also kinnear:siegel}
}

@BOOK{siegwart04,
  AUTHOR = {Roland Siegwart and Illah R. Nourbakhsh},
  YEAR = 2004,
  TITLE = {Autonomous Mobile Robots},
  PUBLISHER = {MIT}
}

@MISC{simanndemo,
  YEAR = 1997,
  TITLE = {A demonstration of Simulated Annealing},
  HOWPUBLISHED = {Internet: http://www.taygeta.com/annealing/demo1.html}
}

@INPROCEEDINGS{sims94,
  AUTHOR = {Karl Sims},
  YEAR = 1994,
  TITLE = {Evolving virtual creatures},
  BOOKTITLE = {SIGGRAPH '94: Proceedings of the 21st annual conference on
		  Computer graphics and interactive techniques},
  PAGES = {15--22},
  PUBLISHER = {ACM Press},
  DOI = {http://doi.acm.org/10.1145/192161.192167},
  ISBN = {0-89791-667-0}
}

@MISC{singhnorvigcohn97,
  AUTHOR = {Satinder Singh and Peter Norvig and David Cohn},
  YEAR = 1997,
  TITLE = {How to Make Software Agents Do the Right Thing{:} An
		  Introduction to Reinforcement Learning},
  HOWPUBLISHED = {\url{http://www.cs.colorado.edu/~baveja/RLMasses/RL.html}}
}

@ARTICLE{song00competitive,
  AUTHOR = {S. Song and K. Miller and L. Abbott},
  YEAR = 2000,
  TITLE = {Competitive Hebbian learning through spiketime -dependent
		  synaptic plasticity},
  JOURNAL = {Nat. Neurosci.},
  VOLUME = 3,
  PAGES = {919--926}
}

@ARTICLE{songmillerabbott00,
  AUTHOR = {S. Song and K. Miller and L. Abbott},
  YEAR = 2000,
  TITLE = {Competitive Hebbian learning through spiketime -dependent
		  synaptic plasticity},
  JOURNAL = {Nat. Neurosci.},
  VOLUME = 3,
  PAGES = {919--926}
}

@INCOLLECTION{steels01groundingsymbols,
  AUTHOR = {Luc Steels},
  EDITOR = {Angelo Cangelosi and Domenico Parisi},
  YEAR = 2002,
  TITLE = {Grounding Symbols through Evolutionary Language Games},
  BOOKTITLE = {Simulating the Evolution of Language},
  CHAPTER = 10,
  PAGES = {211-226},
  PUBLISHER = {Springer Verlag},
  ADDRESS = {London},
  URL = {
		  http://www.isrl.uiuc.edu/~amag/langev/paper/steels01groundingSymbols.html}
}

@ARTICLE{steels03,
  AUTHOR = {Luc Steels},
  YEAR = 2003,
  TITLE = {Evolving Grounded Communication for Robots},
  JOURNAL = {Trends in Cognitive Sciences},
  VOLUME = 7
}

@INPROCEEDINGS{steels94a,
  AUTHOR = {Luc Steels},
  EDITOR = {Rodney A. Brooks and Pattie Maes},
  MONTH = JUL,
  YEAR = 1994,
  TITLE = {Emergent Functionality in Robotic Agents through On-Line
		  Evolution},
  BOOKTITLE = {Proceedings of the 4th International Workshop on the
		  Synthesis and Simulation of Living Systems \(Artificial
		  Life {IV}\)},
  PAGES = {8--16},
  PUBLISHER = {MIT Press,Cambridge, MA},
  ADDRESS = {Cambridge, MA, USA}
}

@INPROCEEDINGS{steels94b,
  AUTHOR = {Luc Steels},
  EDITOR = {A. G. Cohn},
  MONTH = AUG # {8--12~},
  YEAR = 1994,
  TITLE = {Equilibrium Analysis of Behavior Systems},
  BOOKTITLE = {Proceedings of the Eleventh European Conference on
		  Artificial Intelligence},
  PAGES = {714--718},
  PUBLISHER = {John Wiley and Sons},
  ADDRESS = {Chichester}
}

@INPROCEEDINGS{steels95,
  AUTHOR = {Luc Steels},
  EDITOR = {Victor Lesser},
  YEAR = 1995,
  TITLE = {Perceptually grounded meaning creation},
  BOOKTITLE = {Proceedings of the First International Conference on
		  Multi--Agent Systems},
  PUBLISHER = {MIT Press,Cambridge, MA},
  ABSTRACT = {The paper proposes a mechanism for the spontaneous
		  formation of perceptually grounded meanings under the
		  selectionist pressure of a discrimination task. The
		  mechanism is defined formally and the results of some
		  simulation experiments are reported.}
}

@INPROCEEDINGS{steels:1999:hlb,
  AUTHOR = {L. Steels},
  EDITOR = {I. Wachsmutt and B. Jung},
  YEAR = 1999,
  TITLE = {How Language Bootstraps Cognition},
  BOOKTITLE = {{KogWis99. Proceedings der 4. Fachtagung der Gesellschaft
		  f{\"u}r Kognitionswissenschaft}},
  PAGES = {1--3},
  PUBLISHER = {Infix},
  ADDRESS = {Braunschweig, Germany}
}

@ARTICLE{steels:2001:lga,
  AUTHOR = {L. Steels},
  EDITOR = {N. Shadbolt},
  YEAR = 2001,
  TITLE = {Language Games for Autonomous Robots},
  JOURNAL = {IEEE Intelligent Systems},
  VOLUME = 16,
  NUMBER = 5,
  PAGES = {16--22},
  PUBLISHER = {IEEE},
  ADDRESS = {New York, NY, USA},
  URL = {http://arti.vub.ac.be/steels/games.ps}
}

@INCOLLECTION{steels:2002:bgw,
  AUTHOR = {L. Steels and F. Kaplan},
  EDITOR = {T. Briscoe},
  YEAR = 2002,
  TITLE = {Bootstrapping Grounded Word Semantics},
  BOOKTITLE = {Linguistic evolution through language acquisition: Formal
		  and Computational Models},
  PAGES = {??--??},
  PUBLISHER = {Cambridge University Press},
  ADDRESS = {Cambridge, UK},
  URL = {http://arti.vub.ac.be/steels/ground.ps}
}

@INCOLLECTION{steels:2002:gst,
  AUTHOR = {L. Steels},
  EDITOR = {Angelo Cangelosi and Domenico Parisi},
  YEAR = 2002,
  TITLE = {Grounding Symbols Through Evolutionary Language Games},
  BOOKTITLE = {Simulating the Evolution of Language},
  PAGES = {211--226},
  PUBLISHER = {Springer-Verlag},
  ADDRESS = {Berlin}
}

@ARTICLE{steels:2002:lge,
  AUTHOR = {L. Steels},
  EDITOR = {N. Shadbolt},
  YEAR = 2002,
  TITLE = {Language Games for Emergent Semantics},
  JOURNAL = {IEEE Intelligent Systems},
  VOLUME = 17,
  NUMBER = 1,
  PAGES = {83--85},
  PUBLISHER = {IEEE},
  ADDRESS = {New York}
}

@PHDTHESIS{steinhage97,
  AUTHOR = {A. Steinhage},
  YEAR = 1997,
  TITLE = {Dynamical Systems for the Generation of Navigation
		  Behavior},
  SCHOOL = {Ruhr-Universit at Bochum, Germany}
}

@INCOLLECTION{sutbar:90,
  AUTHOR = {Richard S. Sutton and Andrew G. Barto},
  EDITOR = {J. W. Moore and M. Gabriel},
  YEAR = 1990,
  TITLE = {Time-derivative models of {P}avlovian reinforcement},
  BOOKTITLE = {Learning and Computational Neuroscience},
  PUBLISHER = {MIT Press}
}

@ARTICLE{sutton00,
  AUTHOR = {Sutton, R.S and McAllester, D. and Singh, S. and Mansour,
		  Y.},
  YEAR = 2000,
  TITLE = {Policy Gradient Methods for Reinforcement Learning with
		  Function Approximation},
  JOURNAL = {Advances in Neural Information Processing Systems},
  VOLUME = 12,
  PAGES = {1057-1063}
}

@INPROCEEDINGS{sutton90a,
  AUTHOR = {Richard S. Sutton},
  MONTH = JUN,
  YEAR = 1990,
  TITLE = {Integrated Architectures for Learning, Planning and
		  Reacting Based on Approximating Dynamic Programming},
  BOOKTITLE = {Proceedings of the Seventh International Conference on
		  Machine Learning}
}

@INPROCEEDINGS{sutton91,
  AUTHOR = {Richard S. Sutton},
  YEAR = 1991,
  TITLE = {Reinforcement Learning Architectures for Animats},
  BOOKTITLE = {Proceedings of th International Workshop on the Simulation
		  of Adaptive Behavior: From Animals to Animats},
  PAGES = {288--296},
  PUBLISHER = {MIT Press},
  ADDRESS = {Cambridge, Massachusetts}
}

@BOOK{sutton98,
  AUTHOR = {R. S. Sutton},
  YEAR = 1998,
  TITLE = {Reinforcement Learning: An Introduction},
  PUBLISHER = {MIT Press/Bradford Books}
}

@ARTICLE{sutton:temp-d,
  AUTHOR = {Richard S. Sutton},
  YEAR = 1988,
  TITLE = {Learning to Predict by the Methods of Temporal
		  Differences},
  JOURNAL = {Machine Learning},
  VOLUME = 3,
  PAGES = {9--44}
}

@INPROCEEDINGS{ta00,
  AUTHOR = {G. Taga},
  YEAR = 2000,
  TITLE = {Nonlinear dynamics of the human motor control - real-time
		  and anticipatory adaptation of locomotion and development
		  of movements},
  BOOKTITLE = {Proceedings of the International Symposium on Adaptive
		  Motion of Animals and Machines},
  KEY = {Ta00}
}

@INPROCEEDINGS{taga94,
  AUTHOR = {Gentaro Taga},
  YEAR = 1994,
  TITLE = {Emergence of bipedal locomotion through entrainment among
		  the neuro-musculo-skeletal system and the environment},
  BOOKTITLE = {Proceedings of the NATO advanced research workshop and EGS
		  topical workshop on Chaotic advection, tracer dynamics and
		  turbulent dispersion},
  PAGES = {190--208},
  PUBLISHER = {Elsevier North-Holland, Inc.},
  ADDRESS = {New York, NY, USA},
  DOI = {http://dx.doi.org/10.1016/0167-2789(94)90283-6},
  LOCATION = {Sereno di Gavi, Italy}
}

@ARTICLE{tani01,
  AUTHOR = {Tani, Jun},
  YEAR = 2001,
  TITLE = {{Learning to generate articulated behavior through the
		  bottom-up and the top-down interaction processes.}},
  JOURNAL = {Neural Networks. The Official Journal of the International
		  Neural Network Society, European Neural Network Society,
		  Japanese Neural Network Society.},
  VOLUME = 16,
  NUMBER = 1,
  PAGES = {11-23},
  ABSTRACT = {{Summary: A novel hierarchical neural network architecture
		  for sensory-motor learning and behavior generation is
		  proposed. Two levels of forward model neural networks are
		  operated on different time scales while parametric
		  interactions are allowed between the two network levels in
		  the bottom-up and top-down directions. The models are
		  examined through experiments of behavior learning and
		  generation using a real robot arm equipped with a vision
		  system. The results of the learning experiments showed that
		  the behavioral patterns are learned by self-organizing the
		  behavioral primitives in the lower level and combining the
		  primitives sequentially in the higher level. The results
		  contrast with prior work by Pawelzik et al. [Neural
		  Comput. 8, 340 (1996)], Tani and Nolfi
		  [From animals to animats, 1998], and
		  Wolpert and Kawato [Neural Networks 11, 1317
		  (1998)] in that the primitives are represented in a
		  distributed manner in the network in the present scheme
		  whereas, in the prior work, the primitives were localized
		  in specific modules in the network. Further experiments of
		  on-line planning showed that the behavior could be
		  generated robustly against a background of real world noise
		  while the behavior plans could be modified flexibly in
		  response to changes in the environment. It is concluded
		  that the interaction between the bottom-up process of
		  recalling the past and the top-down process of predicting
		  the future enables both robust and flexible situated behavior.}},
  KEYWORDS = {{Learning; Behavior; Articulation; Chunking}},
  LANGUAGE = {English},
  CLASSMATH = {{*I.2.6 Learning F.1.1 Models of Computation J.3 Life and
		  Medical Sciences (Computer Applications)}}
}

@INPROCEEDINGS{tani04,
  AUTHOR = {Jun Tani},
  EDITOR = {M. V. Butz and O. Sigaud and P. Gerard},
  YEAR = 2004,
  TITLE = {Symbols and Dynamics in Embodied Cognition: Revisiting a
		  Robot Experiment},
  BOOKTITLE = {Anticipatory Behavior in Adaptive Learning Systems},
  PAGES = {167-178},
  PUBLISHER = {Springer-Verlag}
}

@ARTICLE{taniito03,
  AUTHOR = {Jun Tani and Masato Ito},
  YEAR = 2003,
  TITLE = {Self-Organization of Behavioral Primitives as Multiple
		  Attractor Dynamics: A Robot Experiment},
  JOURNAL = {IEEE Transactions of on Systems, Man, and Cybernetics Part
		  A: Systems and Humans},
  VOLUME = 33,
  NUMBER = 4,
  PAGES = {481-488}
}

@INPROCEEDINGS{tedrake04,
  AUTHOR = {Russ Tedrake and Teresa Weirui Zhang and H. Sebastian
		  Seung},
  YEAR = 2004,
  TITLE = {Stochastic Policy Gradient Reinforcement Learning on a
		  Simple 3D Biped},
  BOOKTITLE = {Proceedings of the {IEEE} International Conference on
		  Intelligent Robots and Systems {(IROS)}},
  PAGES = {2849-2854}
}

@MISC{test,
  AUTHOR = {Test},
  YEAR = {test},
  TITLE = {Test},
  NOTE = {test}
}

@ARTICLE{thompson:1996:ueh,
  AUTHOR = {A. Thompson and I. Harvey and P. Husbands},
  YEAR = 1996,
  TITLE = {Unconstrained Evolution and Hard Consequences},
  JOURNAL = {Lecture Notes in Computer Science},
  VOLUME = 1062,
  PAGES = {136--??}
}

@BOOK{tolman32,
  AUTHOR = {E. C. Tolman},
  YEAR = 1932,
  TITLE = {Purposive Behavior in Animals and Men},
  PUBLISHER = {Appleton-Century-Crofts}
}

@BOOK{tschacher03,
  AUTHOR = {W. Tschacher and J. Dauwalder},
  YEAR = 2003,
  TITLE = {The Dynamical Systems Approach to Cognition: Concepts and
		  Empirical Paradigms Based on Self-Organization, Embodiment,
		  and Coordination Dynamics},
  PUBLISHER = {World Scientific Publishing Company},
  ADDRESS = {Singapore}
}

@ARTICLE{turrigiano04,
  AUTHOR = {G. G. Turrigiano and S. B. Nelson},
  YEAR = 2004,
  TITLE = {Homeostatic Plasticity in the Developing Nervous System},
  JOURNAL = {Nature Reviews Neuroscience},
  VOLUME = 5,
  PAGES = {97--107}
}

@ARTICLE{turrigiano99,
  AUTHOR = {Gina G. Turrigiano},
  YEAR = 1999,
  TITLE = {Homeostatic Plasticity in Neuronal Networks: The More
		  Things Change, the More They Stay the Same},
  JOURNAL = {Trends in Neuroscience},
  VOLUME = 22,
  PAGES = {221-228},
  ABSTRACT = {During learning and development, neural circuitry is
		  refined, in part, through changes in the number and
		  strength of synapses. Most studies of long-term changes in
		  synaptic strength have concentrated on Hebbian mechanisms,
		  where these changes occur in a synapse-specific manner.
		  While Hebbian mechanisms are important for modifying
		  neuronal circuitry selectively, they might not be
		  sufficient because they tend to destabilize the activity of
		  neuronal networks. Recently, several forms of homeostatic
		  plasticity that stabilize the properties of neural circuits
		  have been identified. These include mechanisms that
		  regulate neuronal excitability, stabilize total synaptic
		  strength, and influence the rate and extent of synapse
		  formation. These forms of homeostatic plasticity are likely
		  to go 'hand-in-glove' with Hebbian mechanisms to allow
		  experience to modify the properties of neuronal networks
		  selectively.}
}

@ARTICLE{turrigianonelson00,
  AUTHOR = {Gina G. Turrigiano and S.B. Nelson},
  YEAR = 2000,
  TITLE = {Hebb and Homeostasis in Neuronal Plasticity},
  JOURNAL = {Current Opinion in Neurobiology},
  VOLUME = 10,
  PAGES = {358-364},
  ABSTRACT = {The positive-feedback nature of Hebbian plasticity can
		  destabilize the properties of neuronal networks. Recent
		  work has demonstrated that this destabilizing influence is
		  counteracted by a number of homeostatic plasticity
		  mechanisms that stabilize neuronal activity. Such
		  mechanisms include global changes in synaptic strengths,
		  changes in neuronal excitability, and the regulation of
		  synapse number. These recent studies suggest that Hebbian
		  and homeostatic plasticity often target the same molecular
		  substrates, and have opposing effects on synaptic or
		  neuronal properties. These advances significantly broaden
		  our framework for understanding the effects of activity on
		  synaptic function and neuronal excitability.}
}

@BOOK{vankampen81,
  AUTHOR = {N. G. van Kampen},
  YEAR = 1981,
  TITLE = {Stochastic Processes in Physics and Chemistry},
  PUBLISHER = {Elsevier}
}

@BOOK{vapnik95,
  AUTHOR = {V. N. Vapnik},
  YEAR = 1995,
  TITLE = {The nature of statistical learning theory},
  PUBLISHER = {Springer}
}

@ARTICLE{vapnik:1997:svm,
  AUTHOR = {V. N. Vapnik},
  YEAR = 1997,
  TITLE = {The Support Vector Method},
  JOURNAL = {Lecture Notes in Computer Science},
  VOLUME = 1327,
  PAGES = {263--??},
  ISSN = {0302-9743},
  BIBDATE = {Tue Apr 28 08:51:33 MDT 1998},
  CODEN = {LNCSD9},
  ACKNOWLEDGEMENT = ACK-NHFB
}

@TECHREPORT{vleukokov93,
  AUTHOR = {J. M. Vleugels and J. N. Kok and M. H. Overmars},
  YEAR = 1993,
  TITLE = {Motion planning using a colored {K}ohonen map.},
  INSTITUTION = {Utrecht University Technical Report RUU-CS-93-83}
}

@BOOK{vonrandow97,
  AUTHOR = {Gero von Randow},
  YEAR = 1997,
  TITLE = {Roboter -- unsere n\"a chsten Verwandten},
  PUBLISHER = {Rowohlt}
}

@MISC{vorlml,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {M}aschinelles {L}ernen.
		  {U}niversit{\"a}t{L}eipzig {I}nstitut f{\"u}r {I}nformatik.
		  Kap.},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/}}
}

@MISC{vorlml1,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {M}aschinelles {L}ernen.
		  {U}niversit{\"a}t{L}eipzig {I}nstitut f{\"u}r {I}nformatik.
		  Kap.1: Einf\"uhrung.},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/ml-intr.ps.Z}}
}

@MISC{vorlml2,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {M}aschinelles {L}ernen.
		  {U}niversit{\"a}t{L}eipzig {I}nstitut f{\"u}r {I}nformatik.
		  Kap. 2: Parameteradaptive {L}ernverfahren},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/{~}der/Vorlesungen/ml-parad.ps.Z}}
}

@MISC{vorlml3,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {M}aschinelles {L}ernen.
		  {U}niversit{\"a}t{L}eipzig {I}nstitut f{\"u}r {I}nformatik.
		  Kap. 3: >Klassifikationslernen},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/{~}der/Vorlesungen/ml-klass.ps.Z}}
}

@MISC{vorlneuroinformatik,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {U}niversit{\"a}t{L}eipzig,
		  {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/skrpt-ni.html}}
}

@MISC{vorlneuroinformatik1,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 1.
		  {U}niversit{\"a}t{L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/grundlgn.ps.Z}}
}

@MISC{vorlneuroinformatik2,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 2.
		  {U}niversit{\"a}t{L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/el-proz.ps.Z}}
}

@MISC{vorlneuroinformatik3,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 3.
		  {U}niversit{\"a}t{L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/ni-feed.ps.Z}}
}

@MISC{vorlneuroinformatik4,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 4.
		  {U}niversit{\"a}t{L}eipzig, {I}nstitut f{\"u}r {I}nformatik}
}

@MISC{vorlneuroinformatik5,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 5.
		  {U}niversit{\"a}t{L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/ni-zeit.ps.Z}}
}

@MISC{vorlneuroinformatik6,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 6.
		  {U}niversit{\"a}t{L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/ni-attraktor.ps.Z}}
}

@MISC{vorlneuroinformatik7,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 7. .
		  {U}niversit{\"a}t {L}eipzig, {I}nstitut f{\"u}r
		  {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/ni-som.ps.Z}}
}

@MISC{vorlneuroinformatik8,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 8.
		  {U}niversit{\"a}t {L}eipzig, {I}nstitut f{\"u}r
		  {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/ni-automat.ps.Z}}
}

@MISC{vorlneuroinformatik9,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {N}euroinformatik. {K}apitel 9: Neuro-Fuzzy
		  Verfahren. {U}niversit{\"a}t {L}eipzig, {I}nstitut f{\"u}r
		  {I}nformatik}
}

@MISC{vorlrobbew,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {U}niversit{\"a}t{L}eipzig {I}nstitut
		  f{\"u}r {I}nformatik. Kap. 2.4: Die Bewegungskomponente},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/rob.beweg.ps.Z}}
}

@MISC{vorlrobotik,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {U}niversit{\"a}t {L}eipzig
		  {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/skrpt-rob.html}}
}

@MISC{vorlrobotik3,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {K}apitel 3. {U}niversit{\"a}t
		  {L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/rob-neuro.ps.Z}}
}

@MISC{vorlrobotik4,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {K}apitel 4: Roboterevolution.
		  {U}niversit{\"a}t {L}eipzig, {I}nstitut f{\"u}r
		  {I}nformatik}
}

@MISC{vorlrobotik5,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {K}apitel 5: Selbstorganisation und
		  {A}rtificial {L}ife. {A}nimaten. {U}niversit{\"a}t
		  {L}eipzig, {I}nstitut f{\"u}r {I}nformatik}
}

@MISC{vorlrobotik6,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {K}apitel 6. {U}niversit{\"a}t
		  {L}eipzig, {I}nstitut f{\"u}r {I}nformatik}
}

@MISC{vorlrobotik7,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {K}apitel 7. {U}niversit{\"a}t
		  {L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/rob-kinmatik.ps.Z}}
}

@MISC{vorlrobotik8,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {K}apitel 8. {U}niversit{\"a}t
		  {L}eipzig, {I}nstitut f{\"u}r {I}nformatik},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/rob-dynamik.ps.Z}}
}

@MISC{vorlrobplan,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {U}niversit{\"a}t{L}eipzig {I}nstitut
		  f{\"u}r {I}nformatik Kap. 2.3: Die Planungskomponente},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/rob-plan.ps.Z}}
}

@MISC{vorlrobsens,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik. {U}niversit{\"a}t{L}eipzig {I}nstitut
		  f{\"u}r {I}nformatik Kap. 2.1: Die Sensorkomponente},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/~der/Vorlesungen/rob-sensor.ps.Z}}
}

@MISC{vorlrobwiss,
  AUTHOR = {R. Der},
  TITLE = {Vorlesung {R}obotik.{U}niversit{\"a}t{L}eipzig {I}nstitut
		  f{\"u}r {I}nformatik. Kap. 2.2: Die Wissenskomponente},
  HOWPUBLISHED = {\url{http://www.informatik.uni-leipzig.de/{~}der/Vorlesungen/rob.wissen.ps.Z}}
}

@BOOK{wapnik79,
  AUTHOR = {W. N. Wapnik and A. J. Tscherwonenkis},
  YEAR = 1979,
  TITLE = {Theorie der Zeichenerkennung},
  PUBLISHER = {Akademie--Verlag}
}

@INCOLLECTION{werbos:menu,
  AUTHOR = {Paul J. Werbos},
  EDITOR = {W. Thomas Miller and Richard S. Sutton and Paul J.
		  Werbos},
  YEAR = 1990,
  TITLE = {A Menu of Designs for Reinforcement Learning over Time},
  BOOKTITLE = {Neural networks for control},
  CHAPTER = 3,
  PAGES = {67--95},
  PUBLISHER = {M.I.T. Press},
  ADDRESS = {Cambridge, Mass}
}

@ARTICLE{wi98,
  AUTHOR = {M. M. Williamson},
  YEAR = 1998,
  TITLE = {Neural Control of Rhythmic Arm Movements},
  JOURNAL = {Neural Networks},
  VOLUME = 11,
  PAGES = {1379-1394},
  KEY = {Wi98}
}

@INPROCEEDINGS{williams04,
  AUTHOR = {H. Williams},
  EDITOR = {S. Schaal},
  YEAR = 2004,
  TITLE = {Homeostatic Plasticity in Recurrent Neural Networks},
  BOOKTITLE = {From Animals to Animats: Proceedings of the 8th Intl.
		  Conf. On Simulation of Adaptive Behavior},
  SERIES = 8,
  VOLUME = 8,
  NUMBER = 8,
  PUBLISHER = {MIT Press}
}

@ARTICLE{williams92,
  AUTHOR = {Williams, Ronald J.},
  YEAR = 1992,
  TITLE = {Simple Statistical Gradient-Following Algorithms for
		  Connectionist Reinforcement Learning},
  JOURNAL = {Machine Learning},
  VOLUME = 8,
  PAGES = {229 - 256}
}

@ARTICLE{wissej2002,
  AUTHOR = {Wiskott, Laurenz and Sejnowski, Terrence},
  YEAR = 2002,
  TITLE = {Slow Feature Analysis: Unsupervised Learning of
		  Invariances.},
  JOURNAL = {Neural Computation},
  VOLUME = 14,
  NUMBER = 4,
  PAGES = {715--770},
  ABSTRACT = {Invariant features of temporally varying signals are
		  useful for analysis and classification. Slow feature
		  analysis (SFA) is a new method for learning invariant or
		  slowly varying features from a vectorial input signal. SFA
		  is based on a non-linear expansion of the input signal and
		  application of principal component analysis to this
		  expanded signal and its time derivative. It is guaranteed
		  to find the optimal solution within a family of functions
		  directly and can learn to extract a large number of
		  decorrelated features, which are ordered by their degree of
		  invariance. SFA can be applied hierarchically to process
		  high dimensional input signals and to extract complex
		  features. Slow feature analysis is applied first to complex
		  cell tuning properties based on simple cell output
		  including disparity and motion. Then, more complicated
		  input-output functions are learned by repeated application
		  of SFA. Finally, a hierarchical network of SFA-modules is
		  presented as a simple model of the visual system. The same
		  unstructured network can learn translation, size, rotation,
		  contrast, or, to a lesser degree, illumination invariance
		  for one-dimensional objects, depending only on the training
		  stimulus. Surprisingly, only a few training objects
		  sufficed to achieve good generalization to new objects. The
		  generated representation is suitable for object
		  recognition. Performance degrades, if the network is
		  trained to learn multiple invariances simultaneously.},
  URLABSTRACT = {
		  http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/WisSej2002.html},
  URLABSTRACT2 = {http://www.cnl.salk.edu/~wiskott/Abstracts/WisSej2002.html},
  URLPAPER = {
		  http://itb.biologie.hu-berlin.de/~wiskott/Publications/WisSej2002-LearningInvariances-NC.ps.gz},
  URLPAPER2 = {
		  http://www.cnl.salk.edu/~wiskott/Publications/WisSej2002-LearningInvariances-NC.ps.gz}
}

@ARTICLE{wnekmichalski94,
  AUTHOR = {Wnek, J. and Michalski, R.S.},
  YEAR = 1994,
  TITLE = {Hypothesis-driven Constructive Induction in AQ17-HCI: A
		  Method and Experiments},
  VOLUME = {Machine Learning},
  NUMBER = 14,
  PAGES = {139-168}
}

@ARTICLE{wolpert98,
  AUTHOR = {Daniel M. Wolpert and R. Chris Miall and Mitsuo Kawato},
  YEAR = 1998,
  TITLE = {Internal Models in the Cerebellum},
  JOURNAL = {Trends in Cognitive Sciences},
  VOLUME = 2,
  PAGES = {338 - 347}
}

@ARTICLE{wolpert99,
  AUTHOR = {D. Wolpert and M. Kawato},
  YEAR = 1999,
  TITLE = {Multiple Paired Forward and Inverse Models for Motor
		  Control},
  JOURNAL = {Neural Networks},
  VOLUME = 11,
  PAGES = {1317-1329}
}

@ARTICLE{wolpertmiall98,
  AUTHOR = {D. M. Wolpert and R. C. Miall and M. Kawato},
  YEAR = 1998,
  TITLE = {Internal Models in the Cerebellum},
  JOURNAL = {Trends in Cognitive Sciences},
  VOLUME = 2,
  NUMBER = 9
}

@INPROCEEDINGS{yokoiakywm03,
  AUTHOR = {Hiroshi Yokoi and Alejandro Hernandez Arieta and Ryu Katoh
		  and Wenwei Yu and Ichiro Watanabe and Masaharu Maruishi},
  YEAR = 2003,
  TITLE = {Mutual Adaptation in a Prosthetics Application.},
  BOOKTITLE = {Embodied Artificial Intelligence},
  PAGES = {146-159},
  CROSSREF = {DBLP:conf/adhoc-now/2003},
  EE = {
		  http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=146},
  BIBSOURCE = {DBLP, http://dblp.uni-trier.de}
}

@ARTICLE{yuan1995,
  AUTHOR = {Yufei Yuan and Michael J. Shaw},
  YEAR = 1995,
  TITLE = {Induction of Fuzzy Decision Trees},
  JOURNAL = {Fuzzy Sets and Systems},
  VOLUME = 69,
  NUMBER = 2,
  PAGES = {125--139}
}

@ARTICLE{ziemke98,
  AUTHOR = {Tom Ziemke},
  YEAR = 1998,
  TITLE = {Adaptive Behavior in Autonomous Agents},
  JOURNAL = {Presence: Teleoperators and Virtual Environments},
  VOLUME = 7,
  PAGES = {564-587},
  ABSTRACT = {This paper provides an overview of the bottom-up approach
		  to AI, commonly referred to as behavior-oriented AI. The
		  behavior-oriented approach, with its focus on the
		  interaction between autonomous agents and their
		  environments, is introduced by contrasting it with the
		  traditional approach of knowledge-based AI. Different
		  notions of autonomy are discussed, and key problems of
		  generating adaptive and complex behavior are identified. A
		  number of techniques for the generation of behavior are
		  introduced and evaluated regarding their potential for
		  realizing different aspects of autonomy, as well as
		  adaptivity and complexity of behavior. It is concluded
		  that, in order to realize truly autonomous and intelligent
		  agents, the behavior-oriented approach will have to focus
		  even more on lifelike qualities in both agents and
		  environments. (Author)}
}

@BOOK{zimbardo95,
  AUTHOR = {P. G. Zimbardo},
  YEAR = 1995,
  TITLE = {Psychologie},
  PUBLISHER = {Springer}
}