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rules which are activatable by a number of different
schemes. There are no shared data structures across
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message passing. All rules are assumed to run in parallel
and asynchronously. It includes the earlier notions of
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ADDRESS = {Cambridge, Massachusetts},
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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
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experiences to accomplish progressively more abstract
tasks. Past experience suggests that in attempting to build
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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}
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@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}
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@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}
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@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--??}
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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}
}
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AUTHOR = {C. G. Langton},
YEAR = 1990,
TITLE = {Computation at the edge of chaos},
JOURNAL = {Physica D},
VOLUME = 42
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@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}
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@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.}
}
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AUTHOR = {Y. Linde and A. Buzo and R. M. Gray},
YEAR = 1980,
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PAGES = {84-95}
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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}
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@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}
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@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}
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@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}
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@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}
}
@ARTICLE{maasmarkram02,
AUTHOR = {Wolfgang Maass and Henry Markram},
YEAR = 2002,
TITLE = {Synapses as dynamic memory buffers},
JOURNAL = {Neural Netw.},
VOLUME = 15,
NUMBER = 2,
PAGES = {155--161},
PUBLISHER = {Elsevier Science Ltd.},
ADDRESS = {Oxford, UK, UK},
ISSN = {0893-6080},
DOI = {http://dx.doi.org/10.1016/S0893-6080(01)00144-7}
}
@INCOLLECTION{maass:98f,
AUTHOR = {W. Maass},
EDITOR = {W. Maass and C.~M.~Bishop},
YEAR = 1999,
TITLE = {Computing with spiking neurons},
BOOKTITLE = {Pulsed Neural Networks},
PAGES = {55--85},
PUBLISHER = {MIT Press (Cambridge)}
}
@INCOLLECTION{maasszador:98a,
AUTHOR = {W. Maass and A. Zador},
EDITOR = {W. Maass and C. Bishop},
YEAR = 1998,
TITLE = {Computing and learning with dynamic synapses},
BOOKTITLE = {Pulsed Neural Networks},
PAGES = {321-336},
PUBLISHER = {MIT-Press (Cambridge)}
}
@ARTICLE{mach:watkins+dayan:1992,
AUTHOR = {Christopher J.C.H. Watkins and Peter Dayan},
YEAR = 1992,
TITLE = {Technical Note {Q}-Learning},
JOURNAL = {Machine Learning},
VOLUME = 8,
PAGES = 279
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@INPROCEEDINGS{macinnes2004,
AUTHOR = {Macinnes, I. and Di Paolo, E.},
EDITOR = {Pollack, J. and Bedau, M. and Husbands, P. and Ikegami, T.
and Watson, R.A.},
YEAR = 2004,
TITLE = {Crawling out of the simulation: Evolving real robot
morphologies using cheap, reusable modules},
BOOKTITLE = {Proceedings of the Ninth International Conference on the
Simulation and Synthesis of Living Systems, ALIFE'9},
PUBLISHER = {MIT Press, Cambridge MA}
}
@PHDTHESIS{malthouse95,
AUTHOR = {E. Malthouse},
YEAR = 1995,
TITLE = {Nonlinear partial least squares},
SCHOOL = {Northwestern University, Illinois},
URL = {http://skew2.kellogg.nwu.edu/~ecm/ftp/dissert.html}
}
@ARTICLE{malthouse97,
AUTHOR = {Edward Malthouse and Ajit Tamhane and Richard Mah},
YEAR = 1997,
TITLE = {Nonlinear Partial Least Squares},
JOURNAL = {Computers in Chemical Engineering},
VOLUME = 12,
NUMBER = 8,
PAGES = {875-890}
}
@MASTERSTHESIS{martius05,
AUTHOR = {Georg Martius},
YEAR = 2005,
TITLE = {Aktives Lernen in der Sensomotorischen Schleife},
SCHOOL = {Universit\"at Leipzig}
}
@ARTICLE{mataric95,
AUTHOR = {Maja J. Mataric and Dave Cliff},
MONTH = OCT,
YEAR = 1996,
TITLE = {Challenges in Evolving Controllers for Physical Robots},
JOURNAL = {Journal of Robotics and Autonomous Systems},
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}
}
@ARTICLE{mataric:1995:cecpr,
AUTHOR = {Maja J. Mataric and Dave Cliff},
MONTH = OCT,
YEAR = 1996,
TITLE = {Challenges in Evolving Controllers for Physical Robots},
JOURNAL = {Journal of Robotics and Autonomous Systems},
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}
}
@ARTICLE{mataric:1995:elp,
AUTHOR = {M. J. Mataric},
YEAR = 1995,
TITLE = {Evaluation of Learning Performance of Situated Embodied
Agents},
JOURNAL = {Lecture Notes in Computer Science},
VOLUME = 929,
PAGES = {579--??},
ISSN = {0302-9743},
BIBDATE = {Sat May 11 13:45:32 MDT 1996},
CODEN = {LNCSD9},
ACKNOWLEDGEMENT = ACK-NHFB
}
@ARTICLE{matsuoka85,
AUTHOR = {Matsuoka, Kiyotoshi},
YEAR = 1985,
TITLE = {Sustained Oscillations Generated by Mutually Inhibiting
Neurons with Adaptation},
JOURNAL = {Biol. Cybern.},
VOLUME = 52,
PAGES = {367-376}
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@BOOK{maturana79,
AUTHOR = {Maturana, H. R. and Varela, F. J.},
YEAR = 1979,
TITLE = {Autopoiesis and Cognition: The Realization of the Living},
PUBLISHER = {Reidel},
ADDRESS = {Boston}
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@ARTICLE{mcfarland_ras1997,
AUTHOR = {McFarland, D.J. and Spier, E.},
YEAR = 1997,
TITLE = {Basic cycles, utility and opportunism in self-sufficient
mobile robots},
JOURNAL = {Robotics and Autonomous Systems},
NUMBER = 20,
PAGES = {179-190}
}
@ARTICLE{mcgeer90,
AUTHOR = {McGeer, T.},
YEAR = 1990,
TITLE = {Passive Dynamic Walking},
JOURNAL = {International Journal of Robotics Research},
VOLUME = {9, No., 2,},
PAGES = {62-82}
}
@INPROCEEDINGS{mehtamanis1995b,
AUTHOR = {Manish Mehta and Jorma Rissanen and Rakesh Agrawal},
MONTH = AUG,
YEAR = 1995,
TITLE = {{MDL}-Based Decision Tree Pruning},
BOOKTITLE = {Proceedings of the First International Conference on
Knowledge Discovery and Data Mining (KDD'95)},
PAGES = {216--221},
KEYWORDS = {Data Mining, Classification, Decision-Trees, MDL},
URL = {http://www.cs.wisc.edu/~mmehta/papers/kdd95.ps},
ABSTRACT-URL = {http://www.cs.wisc.edu/~/mmehta/abstracts/kdd95},
SCOPE = {mine}
}
@ARTICLE{metropolis53,
AUTHOR = {N. Metropolis and A. W. Rosenbluth and M. N. Rosenbluth
and A. H. Teller and E. Teller},
YEAR = 1953,
TITLE = {},
JOURNAL = {J. Chem. Phys. 21},
VOLUME = 21,
PAGES = 1087
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@BOOK{michiespiegtayl94,
AUTHOR = {D. Michie and D. J. Spiegelhalter and C. C. Taylor},
YEAR = 1994,
TITLE = {Machine learning, neural and statistical classification},
NOTE = {STATLOG-{R}eport},
PUBLISHER = {Ellis {H}oorwood}
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@TECHREPORT{miller92role,
AUTHOR = {Kenneth D. Miller and David J. C. MacKay},
YEAR = 1992,
TITLE = {The Role of Constraints in {H}ebbian Learning},
NUMBER = {CNS Memo 19},
ADDRESS = {Pasadena, California}
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@ARTICLE{mitchell03,
AUTHOR = {Mitchell, S.J. and Silver, R. A.},
YEAR = 2003,
TITLE = {Shunting Inhibition Modulates Neuronal Gain During
Synaptic Excitation},
JOURNAL = {Neuron},
VOLUME = 38,
PAGES = {433-45.}
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@INPROCEEDINGS{miyafu94,
AUTHOR = {S. Miyakoshi and M. Yamakita and K. Furuta},
YEAR = 1994,
TITLE = {Juggling control using neural oscillators},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems},
PAGES = {1186-1193},
KEY = {MiYaFu94}
}
@MISC{moreausontag02,
AUTHOR = {Luc Moreau and Eduardo Sontag},
YEAR = 2002,
TITLE = {Balancing at the Border of Instability},
HOWPUBLISHED = {submitted}
}
@ARTICLE{moreausontagarcak,
AUTHOR = {Luc Moreau and E. Sontag and M. Arcak},
YEAR = {},
TITLE = {Automated tuning of bifurcations via feedback},
JOURNAL = {to be published},
VOLUME = {},
PAGES = {}
}
@MISC{moreausontagarcak02,
AUTHOR = {Luc Moreau and Eduardo Sontag and Murat Arcak},
YEAR = 2002,
TITLE = {Feedback Tuning of Bifurcations},
HOWPUBLISHED = {submitted}
}
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AUTHOR = {H. M{\"u}hlenbein},
YEAR = 1991,
TITLE = {Darwin's continent cycle theory and its simulation by the
Prisoner's Dilemma},
JOURNAL = {Complex Systems},
VOLUME = 5,
PAGES = {459-478}
}
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AUTHOR = {K.-R.~M{\"u}ller and A.~Smola and G.~R{\"a}tsch and
B.~Sch{\"o}lkopf and J.~Kohlmorgen and V.~Vapnik},
EDITOR = {W.~Gerstner and A.~Germond and M.~Hasler and
J.-D.~Nicoud},
YEAR = 1997,
TITLE = {Using Support Vector Machines for Time Series Prediction},
BOOKTITLE = {Proceedings ICANN'97, Int. Conf. on Artificial Neural
Networks},
SERIES = {LNCS},
VOLUME = 1327,
PAGES = {999--1004},
PUBLISHER = {Springer},
ADDRESS = {Berlin}
}
@INCOLLECTION{muesmoraeschkohvap98,
AUTHOR = {K.-R.~M{\"u}ller and A.~Smola and G.~R{\"a}tsch and
B.~Sch{\"o}lkopf and J.~Kohlmorgen and V.~Vapnik},
EDITOR = {B.~{Sch{\"o}lkopf} and C.~Burges and A.~Smola},
YEAR = 1998,
TITLE = {Using Support Vector Machines for Time Series Prediction},
BOOKTITLE = {Advances in Kernel Methods --- Support Vector Learning},
NOTE = {to appear},
PUBLISHER = {MIT Press},
ADDRESS = {Cambridge, MA}
}
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AUTHOR = {A. Murbach},
YEAR = 1996,
TITLE = {An integrative computational model for associative
learning},
JOURNAL = {Kognitionswissenschaft},
VOLUME = 5,
PAGES = {175-138}
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@ARTICLE{nakanishi04,
AUTHOR = {Nakanishi, J. and Morimoto, J. and Endo, G. and Cheng, G.
and Schaal, S. and Kawato, M.},
YEAR = 2004,
TITLE = {Learning from Demonstration and Adaptation of Biped
Locomotion},
JOURNAL = {Robotics and Autonomous Systems},
VOLUME = 47,
PAGES = {79-91}
}
@ARTICLE{nakanishi04a,
AUTHOR = {Nakanishi, J. and Farrell, J. A.and Schaal, S.},
YEAR = 2004,
TITLE = {Learning Composite Adaptive Control for a Class of
Nonlinear Systems},
JOURNAL = {IEEE International Conference on Robotics and Automation},
PAGES = {2647-2652}
}
@ARTICLE{nc:kistler+hemmen:1999,
AUTHOR = {Werner M. Kistler and J. Leo van Hemmen},
YEAR = 1999,
TITLE = {Short-Term Synaptic Plasticity and Network Behavior},
JOURNAL = {Neural Computation},
VOLUME = 11,
NUMBER = 7,
PAGES = {1579--1594}
}
@ARTICLE{nc:kistler+hemmen:2000,
AUTHOR = {Werner M. Kistler and J. Leo van Hemmen},
YEAR = 2000,
TITLE = {Modeling Synaptic Plasticity in Conjuction with the Timing
of Pre- and Postsynaptic Action Potentials},
JOURNAL = {Neural Computation},
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NUMBER = 2,
PAGES = {385--405}
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@ARTICLE{nc:oore+hinton+dudek:1997,
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YEAR = 1997,
TITLE = {A Mobile Robot That Learns Its Place},
JOURNAL = {Neural Computation},
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NUMBER = 3,
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@INPROCEEDINGS{nehmzow03,
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YEAR = 2003,
TITLE = {Quantitative Description of Robot-Environment Interaction
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BOOKTITLE = {Proc. European Conference on Mobile Robotics {(ECMR)}}
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CHAPTER = 12,
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AUTHOR = {A. E. Nicholson and J. M. Brady},
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Intelligence Proceedings},
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ADDRESS = {Vienna, Austria}
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@INPROCEEDINGS{nips-7:hofmann+buhmann:1995,
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ADDRESS = {Boston/Dordrecht/London}
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TITLE = {Learning Decision Trees for Mapping the Local Environment
in Mobile Robot Navigation},
ABSTRACT = {This paper describes the use of the C4.5 decision tree
learning algorithm in the design of a classifier for a new
approach to the mapping of a mobile robot's local
environment. The decision tree uses the features from the
echoes of an ultrasonic array mounted on the robot to
classify the contours of its local environment. The
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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PAGES = 243
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collecting robot},
NUMBER = {96-04},
INSTITUTION = {Institute of Psychology, C.N.R. Rome}
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mobile robots},
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and Behavior},
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PAGES = {343-364}
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YEAR = 2000,
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YEAR = 1997,
TITLE = {Evolutionary Program induction of binary machine code and
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PUBLISHER = {Krehl Verlag},
ADDRESS = {M\"unster}
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@MISC{oai:citeseerpsu:100230,
AUTHOR = {Bernd Fritzke},
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}
}
@MISC{ode05,
AUTHOR = {Russel Smith},
YEAR = 2005,
TITLE = {Open Dynamics Engine},
HOWPUBLISHED = {\url{http://ode.org/}}
}
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AUTHOR = {P-Y. Oudeyer},
YEAR = 2005,
TITLE = {The Self-Organization of Speech Sounds},
JOURNAL = {Journal of Theoretical Biology},
VOLUME = 233,
NUMBER = 3,
PAGES = {435--449}
}
@MISC{paolo00homeostatic,
AUTHOR = {E. Di Paolo},
YEAR = 2000,
TITLE = {Homeostatic adaptation to inversion in the visual field
and other sensorimotor disruptions},
TEXT = {Di Paolo, E.A., 2000. Homeostatic adaptation to inversion
in the visual field and other sensorimotor disruptions, in
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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YEAR = 2004,
TITLE = {Abnormal Resonance Behavior of the Postural Control Loop
in Parkinson's Disease.},
JOURNAL = {Exp Brain Res.},
VOLUME = {157(3)},
PAGES = {369-76}
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YEAR = 2000,
TITLE = {Postural Control Model Interpretation of Stabilogram
Diffusion Analysis},
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VOLUME = 82,
PAGES = {335-34382}
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YEAR = 2002,
TITLE = {Complex Dynamics and the Structure of Small Neural
Networks Network},
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VOLUME = 13,
PAGES = {195-216}
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YEAR = 2003,
TITLE = {{SO(2)}-Networks as Neural Oscillators},
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PAGES = {144-151},
PUBLISHER = {Springer},
ADDRESS = {Berlin, Heidelberg, New York}
}
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YEAR = 2002,
TITLE = {Dynamical Neural Schmitt Trigger for Robot Control},
SERIES = {Lecture Notes in Computer Science},
VOLUME = 2415,
PUBLISHER = {Springer}
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YEAR = 2002,
TITLE = {The self-organizing consciousness},
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PAGES = {297-330}
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YEAR = 1999,
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YEAR = 2003,
TITLE = {Embodied Artificial Intelligence: Trends and Challenges.},
BOOKTITLE = {Embodied Artificial Intelligence},
PAGES = {1-26},
PUBLISHER = {Bradford Books, MIT Press},
ADDRESS = {Cambridge, MA},
EE = {
http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3139{\&}spage=1}
}
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Kuniyoshi},
YEAR = 2004,
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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YEAR = 1999,
TITLE = {Understanding Intelligence},
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Sensorimotor Dependencies},
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VOLUME = {15(9)},
PAGES = {2029-49}
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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)}
}
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YEAR = 2004,
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KEY = {quinlan}
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ADDRESS = {New York}
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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}
}
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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}
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PAGES = {77--90}
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PAGES = {81--106}
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ADDRESS = {Stuttgart}
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YEAR = {},
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oscillators},
JOURNAL = {Physica D},
NOTE = {In Press},
TYPE = {article},
PDF = {http://birg2.epfl.ch/publications/fulltext/righetti05c.pdf}
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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}}
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PUBLISHER = {Springer}
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YEAR = 1990,
TITLE = {Statistical Mechanics and Phase Transitions in
Clustering},
NUMBER = {CFP-895},
INSTITUTION = {California Institute of Technology}
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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}
}
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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
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YEAR = 1989,
TITLE = {Automatic Text Processing},
PUBLISHER = {Addison Wesley},
ADDRESS = {Massachusetts}
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TITLE = {Introduction to data compression},
PUBLISHER = {Morgan Kaufmann},
ADDRESS = {San Francisco}
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YEAR = 2000,
TITLE = {Synchronized Robot Drumming by Neural Oscillators},
BOOKTITLE = {The International Symposium on Adaptive Motion of Animals
and Machines},
ADDRESS = {Montreal}
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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.}
}
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EDITOR = {Y. Kodratoff},
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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}
}
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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}
}
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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}
}
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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}}
}
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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
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cell tuning properties based on simple cell output
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input-output functions are learned by repeated application
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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
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generated representation is suitable for object
recognition. Performance degrades, if the network is
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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},
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http://www.cnl.salk.edu/~wiskott/Publications/WisSej2002-LearningInvariances-NC.ps.gz}
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TITLE = {Mutual Adaptation in a Prosthetics Application.},
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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}
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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)}
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PUBLISHER = {Springer}
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