Evolution strategy for optimizing parameters in Ms Pac-Man controller ICE Pambush 3

This paper describes an application of Evolutionary Strategy to optimizing ten distance parameters and seven cost parameters in our Ms Pac-Man controller, ICE Pambush 3, which was the winner of the IEEE CIG 2009 competition. Targeting at the first game level, we report our results from 14 possible optimization schemes; arising from combinations of which initial values to chose, those originally used in ICE Pambush 3 or those randomly assigned, and which parameter types to optimize first, the distance parameters, the cost parameters, or both. We have found that the best optimization scheme is to first optimize the distance parameters, with their initial values set to random values, and then the cost parameters with their initial values set to random values. The optimized ICE Pambush 3 using the resulting parameters from this optimization scheme has an improvement of 17% in the performance for the first game level, compared to the original ICE Pambush 3.

[1]  Anthony Brabazon,et al.  Evolving a Ms. PacMan Controller Using Grammatical Evolution , 2010, EvoApplications.

[2]  M. Dianati,et al.  An Introduction to Genetic Algorithms and Evolution , 2002 .

[3]  Simon M. Lucas,et al.  Evolution versus Temporal Difference Learning for learning to play Ms. Pac-Man , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[4]  R. Thawonmas,et al.  Automatic Controller of Ms. Pac-Man and Its Performance: Winner of the IEEE CEC 2009 Software Agent Ms. Pac-Man Competition , 2009 .

[5]  András Lörincz,et al.  Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man , 2007, J. Artif. Intell. Res..

[6]  Simon M. Lucas,et al.  A simple tree search method for playing Ms. Pac-Man , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[7]  Simon M. Lucas,et al.  Evolving a Neural Network Location Evaluator to Play Ms. Pac-Man , 2005, CIG.

[8]  Hisashi Handa.  Constitution of Ms.PacMan player with critical-situation learning mechanism , 2010, Int. J. Knowl. Eng. Soft Data Paradigms.

[9]  H. Handa,et al.  Evolutionary fuzzy systems for generating better Ms.PacMan players , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[10]  Lori L. DeLooze,et al.  Fuzzy Q-learning in a nondeterministic environment: developing an intelligent Ms. Pac-Man agent , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[11]  Marcus Gallagher,et al.  An influence map model for playing Ms. Pac-Man , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.