Abstract
In Mahjong, there are several objectives at play to win the game: to (1) win early, (2) gain a large number of points, (3) avoid losing points, and (4) prevent other players from winning. These objectives often conflict with each other, thereby creating tradeoffs. In this research, we make a evaluating function of Mahjong as a multiobjective optimization. Further, Mahjong requires multimodal behavior, particularly because the first-place player must win early and avoid losing points by the last-place player gaining a large number of points in the final hand. In this paper, we propose an evaluation function for Mahjong in the form of a multi-objective optimization problem. Modular multi-objective neuro-evolution of augmenting topologies (MM-NEAT) is a framework for evolving modular neural networks in which each module defines a separate policy. Evolution optimize these policies and when to use them. Given the above, we focus on two objectives in one-player Mahjong: to (1) win early and (2) gain a large number of points. We also verify the effectiveness of MM-NEAT for Mahjong.
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Ihara, K., Kato, S. (2018). Neuro-Evolutionary Approach to Multi-objective Optimization in One-Player Mahjong. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_43
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DOI: https://doi.org/10.1007/978-3-319-65521-5_43
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