Clever Pac-man

Abstract

In this paper we show how combining fuzzy sets and reinforcement learning a winning agent can be created for the popular Pac-man game. Key elements are the classification of the state into a few fuzzy classes that makes the problem manageable. Pac-man policy is defined in terms of fuzzy actions that are defuzzified to produce the actual Pac-man move. A few heuristics allow making the Pac-man strategy very similar to the Human one. Ghosts agents, on their side, are endowed also with fuzzy behavior inspired by original design strategy. Performance of this Pac-man is shown to be superior to those of other AI-based Pac-man described in the literature.

DOI: 10.3233/978-1-60750-972-1-11

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Cite this paper

@inproceedings{Rossini2011CleverP, title={Clever Pac-man}, author={Nicole Alicia Rossini and Christian Quadri and N. Alberto Borghese}, booktitle={WIRN}, year={2011} }