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Evolving Evil: Optimizing Flocking Strategies Through Genetic Algorithms for the Ghost Team in the Game of Ms. Pac-Man

Liberatore, Federico, Mora, Antonio M., Castillo, Pedro A. and Guervós, Juan Julián Merelo 2014. Evolving Evil: Optimizing Flocking Strategies Through Genetic Algorithms for the Ghost Team in the Game of Ms. Pac-Man. Presented at: EvoApplications 2014, April 23-25. Applications of Evolutionary Computation. Lecture Notes in Computer Science Berlin, Heidelberg: Springer Verlag, pp. 313-324. 10.1007/978-3-662-45523-4_26

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Abstract

Flocking strategies are sets of behavior rules for the interaction of agents that allow to devise controllers with reduced complexity that generate emerging behavior. In this paper, we present an application of genetic algorithms and flocking strategies to control the Ghost Team in the game Ms. Pac-Man. In particular, we define flocking strategies for the Ghost Team and optimize them for robustness with respect to the stochastic elements of the game and effectivity against different possible opponents by means of genetic algorithm. The performance of the methodology proposed is tested and compared with that of other standard controllers. The results show that flocking strategies are capable of modeling complex behaviors and produce effective and challenging agents.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Verlag
ISBN: 978-3-662-45523-4
ISSN: 1611-3349
Last Modified: 10 Jan 2020 11:15
URI: https://orca.cardiff.ac.uk/id/eprint/127425

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