Machine learning and inductive logic programming for multi-agent systems

Authors:
Dimitar Kazakov;Daniel Kudenko
Affiliations:
Univ. of New York, York, United Kingdom;Univ. of New York, York, United Kingdom
Venue:
Mutli-agents systems and applications
Year:
2001

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Abstract

Learning is a crucial ability of intelligent agents. Rather than presenting a complete literature review, we focus in this paper on important issues surrounding the application of machine learning (ML) techniques to agents and multi-agent systems (MAS). In this discussion we move from disembodied ML over single-agent learning to full multi-agent learning. In the second part of the paper we focus on the application of Inductive Logic Programming, a knowledge-based ML technique, to MAS, and present an implemented framework in which multi-agent learning experiments can be carried out.