An integration of individual and social learning in a co-evolutionary approach to the game of Connect-Four
In this paper, we investigate an integration of individual and social learning, utilising co-evolutionary neural networks. Individual learning takes place by playing copies of a player against itself. Social learning allows poor performing players to learn from those players, which are playing at a...
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| 主要な著者: | , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
2007
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/60121/1/RazaliUTHM.pdf http://psasir.upm.edu.my/id/eprint/60121/ |
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| 要約: | In this paper, we investigate an integration of individual and social learning, utilising co-evolutionary neural networks. Individual learning takes place by playing copies of a player against itself. Social learning allows poor performing players to learn from those players, which are playing at a higher level. The networks are evolved via evolutionary strategies with the network output being used as input to a minimax search tree. Our experiments show that learning is taking place at the 99% confidence level. In terms of performance, the co-evolutionary neural network player has the ability to block two adjacent stones of an opponent. |
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