From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience
Title | From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience |
Publication Type | Conference Paper |
Year of Publication | 2011 |
Authors | Raab, M., Wernsdorfer M., Kitzelmann E., & Schmid U. |
Volume | 6830 |
Page(s) | 333-339 |
Other Numbers | 3215 |
Keywords | cognitive architecture, inductive rule learning, symbol grounding, temporal Hebbian learning |
Abstract | In this paper we argue that a philosophically and psychologicallygrounded autonomous agent is able to learn recursive rules frombasic sensorimotor input. A sensorimotor graph of the agents environmentis generated that stores and optimises beneficial motor activationsin evaluated sensor space by employing temporal Hebbian learning. Thisresults in a categorized stream of experience that feeds in a Minervamemory model which is enriched by a time line approach and integratedin the cognitive architecture Psiincluding motivation and emotion.These memory traces feed seamlessly into the inductive rule acquisitiondevice Igor2 and the resulting recursive rules are made accessible in thesame memory store. A combination of cognitive theories from the 1980iesand state-of-the-art computer science thus is a plausible approach to thestill prevailing symbol grounding problem. |
Acknowledgment | This work was partially funded by the Deutscher Akademischer Austausch Diesnst (DAAD) through a postdoctoral fellowship. |
URL | http://www.icsi.berkeley.edu/pubs/ai/RaabWernsdorferEtAl2011.pdf |
Bibliographic Notes | Proceedings of the Fourth International Artificial General Intelligence Conference (AGI 2011), Vol. 6830, pp. 333-339, Mountain View, California |
Abbreviated Authors | M. Raab, M. Wernsdorfer, E. Kitzelmann, and U. Schmid |
ICSI Research Group | AI |
ICSI Publication Type | Article in conference proceedings |