Probabilistic opponent-model search

Information Sciences

Volumn 135, Issue 3-4, 2001, Pages 123-149

  Donkers, H H L M ^a   Uiterwijk, J W H M ^a   Van den Herik, H J ^a  

^a MAASTRICHT UNIVERSITY   (Netherlands)

Author keywords

Games; Heuristic search; LOA; Opponent modelling; Subjective probability

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; GAME THEORY; MATHEMATICAL MODELS; PROBABILITY; TREES (MATHEMATICS);

PROBABILISTIC OPPONENT-MODEL SEARCH;

HEURISTIC PROGRAMMING;

EID: 0035397965     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(01)00133-5     Document Type: Article

References (21)

1. The *-minimax search procedure for trees containing chance nodes
2. Learning models of opponent's strategies in game playing
3. Incorporating opponent models into adversary search
4. Pruning algorithms for multi-model adversary search
5. Potential applications of opponent-model search. Part 1: The domain of applicability
6. Potential applications of opponent-model search. Part 2: Risks and strategies
7. Tutoring strategies in game-tree search
8. KQKR: Speculatively thwarting a human opponent
9. Are there practical alternatives to alpha-beta?
10. An analysis of alpha-beta pruning
11. Learning time allocation using neural networks
12. The efficiency of the alpha-beta search on trees with branch-dependent terminal node scores
13. Non-minimax search strategies for use against fallible opponents
14. Experiments with some programs that search game trees
15. Experiments with the M&N tree-searching program
16. Speculative play in computer chess