• Journal for History of Mathematics
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• v.31 no.3
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• pp.151-166
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• 2018

The rule of the game of Chomp is simple and the existence of a winning strategy can easily be proved. However, the existence tells us nothing about what strategies are winning in reality. Like in Chess or Baduk, many researchers studied the winning moves using computer programs, but no general patterns for the winning actions have not been found. In the paper, we aim to construct practical winning strategies based on backward induction. To do this we develop how to analyze Chomp and prove and find the winning strategies of the simple games of Chomp.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.1
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• pp.1-12
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• 2017

Recently, the Go game has been performed between the 'AlphaGo' of the DeepMind and Lee Sedol, a famous professional Go-player of Korea, which leads to arise a lot of interests in the AI (Artificial Intelligence) research area. Based on the Fuzzy logic of the AI, we have also developed another game's AI, .i.e., the Yutnori game, one of Korean traditional board games. However, it is not easy and simple to consider all the cases of the Yutnori game since it is a non-perfect information game in terms of the AI. Thus, we have developed the Fuzzy-logic-based AI which tries to simulate humans' selections, meaning that the suggested AI has focused on the humans' choices depending on diverse situations in the Yutnori. With our extensive simulations using the suggested Yutnori AI, we have analyzed its performances with respect to 10 Yutnori situations among various scenarios. In conclusion, our suggested AI have demonstrated that 6 out of 10 situations are exactly same with the humans' choices and the rest 4 cases are also similar to that of human's, which reveals that our Fuzz-logic-based Yutnori AI can effectively simulate human's choices.