Paper Info
Title
Probabilistic Opponent-Model Search in Bao
Abstract
In Probabilistic Opponent-Model search (PrOM search) the opponent is modelled by a mixed strategy of N opponent types omega(0) ... omega(N-1). The opponent is assumed to adopt at every move one of the opponent types 0, according to the probability Pr(omega(i)). We hypothesize that PrOM search is a better search mechanism than Opponent-Model search (OM search) and Minimax search. In this paper we investigate two questions: (1) to which extent is PrOM search better than OM search and Minimax search in the game of Bao? and (2) which opponent type is most advantageous to use? To answer the second question we constructed Five evaluation functions which we applied in a tournament consisting of 352,000 games. Our conclusions are twofold: (1) in Bao, PrOM search performs better than OM search and sometimes also better than Minimax search even when no perfect information of the opponent is available, and (2) for an adequate performance of PrOM search, emphasis on the own evaluation function in the opponent model should be higher than assumed so far.
Year
DOI
Venue
2004
10.1007/978-3-540-28643-1_53
Lecture Notes in Computer Science
Keywords
Field
DocType
evaluation function,mixed strategy
Tournament,Prom,Strategy,Computer science,Evaluation function,Minimax search,Artificial intelligence,Probabilistic logic,Perfect information,Minimax problem
Conference
Volume
ISSN
Citations 
3166
0302-9743
2
PageRank 
References 
Authors
0.46
10
3
Name
Order
Citations
PageRank
Jeroen Donkers184.23
H. Jaap van den Herik2861137.51
Jos W. H. M. Uiterwijk329939.17