| Autor: |
Fu Yao, Ding Meng, Li Shuqin, Bo Jianbo, Qi Yizhong |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
2019 Chinese Control And Decision Conference (CCDC). |
| DOI: |
10.1109/ccdc.2019.8832463 |
| Popis: |
The computer chess is an important research direction of artificial intelligence. Amazon chess game is an extremely complicated strategy game and the search algorithm relies heavily on the evaluation function. The design of evaluation function faces great challenges. This process requires a comprehensive consideration of the characteristics of the board and build up a series of complex formulas, which requires a lot of human experience to support. The author tries to apply the reinforcement learning method to the design process of Game of the Amazons evaluation function. Using the self-game generated data sets and reward signals to stimulates network, it could learn how to evaluate the situation of chess board without human experience. Then this neural network can be used as an evaluation function. Finally, through battling with the game of Amazon chess game using traditional evaluation function, it is verified that the evaluation function design method proposed in this paper can reduce the human experience requirement in the design process of Game of the Amazons evaluation function and effectively evaluate the situation. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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