Neural Network Architectures for Solving Imperfect Information Game - Muthusamy, Dharmalingam
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Many FFNN are trained to solve the Double Dummy Bridge Problems (DDBP) in bridge games and they have been formalized in the best defense model, which presents the strongest possible assumptions about the opponent. This is used by human players because modeling the strongest possible opponents provides a lower bound on the pay off that can be expected when the opponents are less informed. In the learning course, besides acquiring this basic information, several other more sophisticated features need to be developed by the learning system.

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Produktbeschreibung
Many FFNN are trained to solve the Double Dummy Bridge Problems (DDBP) in bridge games and they have been formalized in the best defense model, which presents the strongest possible assumptions about the opponent. This is used by human players because modeling the strongest possible opponents provides a lower bound on the pay off that can be expected when the opponents are less informed. In the learning course, besides acquiring this basic information, several other more sophisticated features need to be developed by the learning system.
Autorenporträt
Dr. M Dharmalingam received his Under-Graduate, Post-Graduate and Master of Philosophy degrees from Bharathiar University, Coimbatore in the years 2000, 2004 and 2008 respectively. He has completed his PhD degree in Computer Science in 2015, at Bharathiar University, Coimbatore. Currently he is working as an Associate Professor in Computer Science,