Stochastic Reinforcement in Evolutionary Multi-Agent Game Playing of Dots-and-Boxes

Authors:
Anthony Knittel;Terry Bossomaier;Mike Harre;Allan Snyder
Affiliations:
University of Sydney;Charles Sturt University;Centre for the Mind;Centre for the Mind
Venue:
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Year:
2006

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Abstract

An evolutionary multi-agent system is described that develops a rule-based approach to playing the game Dots and Boxes, under a probabilistic reinforcement learning paradigm. The process and behaviour using probabilistic action selection with a Boltzmann distribution is compared with an alternative technique using an Artificial Economy. The probabilistic system developed was played against a rule-based software opponent, and able to produce behaviour under a self-organising process able to perform better than the software opponent it was trained against.