Selecting evaluation functions in Opponent-Model search
نویسندگان
چکیده
منابع مشابه
Admissibility in Opponent-Model Search
We start with showing experiments in the game Lines of Action (LOA) that hint at the existence of a large risk in using Opponent-Model (OM) search. Then the nature of the risk is investigated and its consequences are unveiled. It appears that, in contrast to Minimax, OM search is sometimes unable to repair large errors in the evaluation function(s). The result is that OM search, even when used ...
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In Probabilistic Opponent-Model search(PrOM search) the opponent is modelled by a mixed strategy of N opponent types ω0 . . . ωN−1. The opponent is assumed to adopt at every move one of the opponent types ωi according to the probability Pr(ωi). 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...
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Strategies anticipating a difference in search depth using opponent-model search
In this contribution we propose a class of strategies which focus on the game as well as on the opponent. Preference is given to the thoughts of the opponent, so that the strategy under investigation might be speculative. We describe a generalization of OM search, called (D; d)-OM search, where D stands for the depth of search by the player and d for the opponent’s depth of search. A known di e...
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Poker is a challenging domain that contains both elements of chance and imperfect information. Though progress has been made in the domain, there is still one major stumbling block on the way to creating a world-class calibre computer player. This is the task of learning how an opponent plays (i.e., opponent modelling) and subsequently coming up with a counter-strategy that can exploit that inf...
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ژورنال
عنوان ژورنال: Theoretical Computer Science
سال: 2005
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2005.09.049