Authors:
Eduardo Alonso
1
and
Esther Mondragón
2
Affiliations:
1
City University London, United Kingdom
;
2
Centre for Computational and Animal Learning Research, United Kingdom
Keyword(s):
Reinforcement Learning, Associative Learning, Agents and Multi-agent Systems.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Autonomous Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
In this position paper we propose to enhance learning algorithms, reinforcement learning in particular, for agents and for multi-agent systems, with the introduction of concepts and mechanisms borrowed from associative learning theory. It is argued that existing algorithms are limited in that they adopt a very restricted view of what “learning” is, partly due to the constraints imposed by the Markov assumption upon which they are built. Interestingly, psychological theories of associative learning account for a wide range of social behaviours, making it an ideal framework to model learning in single agent scenarios as well as in multi-agent domains.