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

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dc.contributor.author Price, William
dc.contributor.author Schrum, Jacob
dc.date.accessioned 2020-11-20T14:56:54Z
dc.date.available 2020-11-20T14:56:54Z
dc.date.issued 2019
dc.identifier.citation 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. en_US
dc.identifier.uri http://hdl.handle.net/11214/241
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher Proceedings of the Congress on Evolutionary Computation (CEC 2019) en_US
dc.subject Computer games en_US
dc.subject Neural nets en_US
dc.subject Neuroevolution en_US
dc.subject Neural networks en_US
dc.title Neuroevolution of Multimodal Ms. Pac-Man Controllers Under Partially Observable Conditions en_US
dc.type Article en_US


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