From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience.
AGI'11: Proceedings of the 4th international conference on Artificial general intelligence(2011)
Abstract
In this paper we argue that a philosophically and psychologically grounded autonomous agent is able to learn recursive rules from basic sensorimotor input. A sensorimotor graph of the agent's environment is generated that stores and optimises beneficial motor activations in evaluated sensor space by employing temporal Hebbian learning. This results in a categorized stream of experience that feeds in a Minerva memory model which is enriched by a time line approach and integrated in the cognitive architecture Psi--including motivation and emotion. These memory traces feed seamlessly into the inductive rule acquisition device Igor2 and the resulting recursive rules are made accessible in the same memory store. A combination of cognitive theories from the 1980ies and state-of-the-art computer science thus is a plausible approach to the still prevailing symbol grounding problem.
MoreTranslated text
Key words
recursive rule,Minerva memory model,memory store,memory trace,autonomous agent,basic sensorimotor input,cognitive architecture,cognitive theory,plausible approach,sensorimotor graph
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined