Asymmetric Game Trees using Dynamic Bayesian Networks
Student thesis: Master thesis (including HD thesis)
- Esben Skov Pedersen
2. term, Computer Science, Master (Master Programme)
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.
Language | English |
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Publication date | 2008 |
Publishing institution | Institut for datalogi |