Neuroevolution of Multimodal Ms. Pac-Man Controllers Under Partially Observable Conditions

Title

Neuroevolution of Multimodal Ms. Pac-Man Controllers Under Partially Observable Conditions

Creator

Price, William

Schrum, Jacob

Date

2020-11-20

Date Available

2020-11-20

Date Issued

2019

Identifier

W. Price and J. Schrum, "Neuroevolution of Multimodal Ms. Pac-Man Controllers Under Partially Observable Conditions," 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 2019, pp. 466-473, doi: 10.1109/CEC.2019.8790278.

uri

https://collections.southwestern.edu/s/suscholar/item/213

Abstract

Ms. Pac-Man is a well-known video game used extensively in AI research. Past research has focused on the standard, fully observable version of Ms. Pac-Man. Recently, a partially observable variant of the game has been used in the Ms. Pac-Man Vs. Ghost Team Competition at the Computational Intelligence and Games (CIG) conference. Restricting Ms. Pac-Man's view makes the game more challenging. Ms. Pac-Man can only see down halls within her direct line of sight. The approach to this domain presented in this paper extends an earlier approach using MM-NEAT, an algorithm for evolving modular neural networks. Experiments using several forms of evolved and human-specified modularity are presented. The best evolved agent uses a human-specified task division with output modules for different situations: no ghosts, edible ghosts, and threat ghosts. This approach placed first at the Ms. Pac-Man Vs. Ghost Team Competition at CIG 2018 against seven other competitors with an average score of 7736.63.

Language

English

Publisher

Proceedings of the Congress on Evolutionary Computation (CEC 2019)

Subject

Computer games

Neural nets

Neuroevolution

Neural networks

Type

Article