MindRACES

Skip to content
You are here: Home » Events » AAAI Symposium - FSS05 » sinList
Sections
Personal tools

CognitiveScienceNews

Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot  2007-04-17
V. Zhumatiy, F. Gomez, M. Hutter, and J. Schmidhuber: Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot, Proc. of the Int'l Conf. on Intelligent Autonomous Systems, IAS-06, Tokyo, 2006.
The cognitive and behavioral mediation of institutions: Towards an account of institutional actions  2007-04-10
Tummolini L., Castelfranchi C. (2006); Cognitive Systems Research, 7(2-3), 307-323.
ZMINDRACESFILE_INDEXING_DOCUMENT  2007-04-16
Zmindracesfile_indexing_document This is a special MindRACES file used to create categories indexes for the others
An Analysis of the Ideomotor Principle and TOTE  2007-04-15
Giovanni Pezzulo, Gianluca Baldassarre, Martin V. Butz, Cristiano Castelfranchi, and Joachim Hoffmann (2006). In Butz M.V., Sigaud O., Pezzulo G., Baldassarre G. (eds.) Proceedings of the Third Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABiALS 2006)
Schema-based design and the AKIRA Schema Language: An Overview  2007-11-15
Pezzulo, G. & Calvi, G. Schema-based design and the AKIRA Schema Language: An Overview Anticipatory Behavior. In Butz, M.; Sigaud, O.; Pezzulo, G. & Baldassarre, G. (ed.) in Adaptive Learning Systems: Advances in Anticipatory Processing, Springer LNAI 4520, 2007
Rule-based evolutionary online learning systems  2007-03-13
Butz, M.V. (2006). Rule-based evolutionary online learning systems: A principled approach to LCS analysis and design. Studies in Fuzziness and Soft Computing Series, Springer Verlag, Berlin-Heidelberg, Germany.
Improving the performance of Pittsburgh learning classifier systems using a default rule  2007-10-30
(2007). In Kovacs, T. et al. (Eds.) Learning Classifier Systems: International Workshops, IWLCS 2003-2005, LNAI 4399, Springer-Verlag, Berlin Heidelberg. 291-307.
Data mining in learning classifier systems: Comparing XCS with GAssist  2007-10-30
(2007). In Kovacs, T. et al. (Eds.) Learning Classifier Systems: International Workshops, IWLCS 2003-2005, LNAI 4399, Springer-Verlag, Berlin Heidelberg. 282-290.
From Actions to Goals and Vice-versa: Theoretical Analysis and Models of the Ideomotor Principle and TOTE  2007-04-15
Pezzulo, G.; Baldassarre, G.; Butz, M.V.; Castelfranchi, C. & Hoffmann, J. From Actions to Goals and Vice-versa: Theoretical Analysis and Models of the Ideomotor Principle and TOTE. In Butz, M.; Sigaud, O.; Pezzulo, G. & Baldassarre, G. (ed.) Anticipatory Behavior in Adaptive Learning Systems: Advances in Anticipatory Processing, Springer LNAI 4520, 2007
 

Powered by Plone

Anticipatory Cognitive Science is a research field that ensembles artificial intelligence, biology, psychology, neurology, engineering and philosophy in order to build anticipatory cognitive systems that are able to face human tasks with the same anticipatory capabilities and performance. In deep: Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures. Its organizational origins are in the mid-1970s when the Cognitive Science Society was formed and the journal Cognitive Science began. Since then, more than sixty universities in North America, Europe, Asia, and Australia have established cognitive science programs, and many others have instituted courses in cognitive science.