A master thesis from Aalborg University
Asymmetric Game Trees using Dynamic Bayesian Networks
Author(s)
Term
2. term
Education
Publication year
2008
Submitted on
2008-07-31
Pages
0 pages
Abstract
This project we improve mini max search by prediciting which move the opponent is most likely to take. This prediction is performed by using a dynamic bayesian network. This network has nodes that represents a number of abstract features ex- tracted from the game. The network is trained using EM-learning based on data from a number of comptuter players playing against each other. These computer players are defined as the weighted sum of the features mentioned before. These weights are found using a genetic approach. The system is able to improve perfor- mance of the worst strategies with- out increasing the search space.
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