Neuroevolution of Multimodal Ms. Pac-Man Controllers Under Partially Observable Conditions
Item
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Title
Neuroevolution of Multimodal Ms. Pac-Man Controllers Under Partially Observable Conditions
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Creator
Price, William
Schrum, Jacob
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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.
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uri
https://collections.southwestern.edu/s/suscholar/item/213
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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.
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Publisher
Proceedings of the Congress on Evolutionary Computation (CEC 2019)
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Subject
Computer games
Neural nets
Neuroevolution
Neural networks