Machine Learning and Inductive Logic Programming for Multi-agent Systems

Dimitar Kazakov; Daniel Kudenko

doi:10.1007/3-540-47745-4_11

Machine Learning and Inductive Logic Programming for Multi-agent Systems

Dimitar Kazakov, Daniel Kudenko

Computer Science

Research output: Contribution to conference › Paper › peer-review

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 multiagent 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.

Original language	Undefined/Unknown
Pages	246-272
DOIs	https://doi.org/10.1007/3-540-47745-4_11
Publication status	Published - 2001

Access to Document

10.1007/3-540-47745-4_11

http://link.springer.de/link/service/series/0558/bibs/2086/20860246.htm

Cite this

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