Using knowledge to control tree searching

Wilkins, David E.

doi:10.1016/0004-3702(82)90009-1

Cited by 18 publications

(6 citation statements)

References 6 publications

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“…However, these works do not address the kind of uncertainty we discussed in the introduction, and thus it does not appear to us that these approaches would be sufficient to accomplish our objectives. Wilkins (1980;1982) uses "knowledge sources" to generate and analyze chess moves for both the player and the opponent. These knowledge sources have a similar intent to the multiagent methods that we describe in this paper-but there are two significant differences.…”

Section: Related Workmentioning

confidence: 99%

A Planning Approach to Declarer Play in Contract Bridge

Smith

Nau

Throop³

1996

Computational Intelligence

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Although game-tree search works well in perfect-information games, it is less suitable for imperfect-information games such as contract bridge. The lack of knowledge about the opponents' possible moves gives the game tree a very large branching factor, making it impossible to search a significant portion of this tree in a reasonable amount of time.This paper describes our approach for overcoming this problem. We represent information about bridge in a task network extended to represent multi-agency and uncertainty. Our game-playing procedure uses this task network to generate game trees in which the set of alternative choices is determined not by the set of possible actions, but by the set of available tactical and strategic schemes.We have tested this approach on declarer play in the game of bridge, in an implementation called Tignum 2. On SO00 randomly generated notrump deals, Tignum 2 beat the strongest commercially available program by 1394 to 1302. with 2304 ties. These results are statistically significant at the a = 0.05 level. Tignum 2 searched an average of only 8745.6 moves per deal in an average time of only 27.5 seconds per deal on a Sun SPARCstation 10. Further enhancements to 'Iignum 2 are currently underway.

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Section: Related Workmentioning

confidence: 99%

A Planning Approach to Declarer Play in Contract Bridge

Smith

Nau

Throop³

1996

Computational Intelligence

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“…only goal-objects defined in the previous cycle, stack management, are considered, as is the case with Emycin). In the second case, actions are preselected: this occurs less frequently because it is often difficult to exhibit intrinsic action criteria which are considered as having high priority (nevertheless, this is the case with the Wilkins chess program [18]. )…”

Section: B Possible Subdivisions Of Transition Selectionmentioning

confidence: 99%

Types of control structure in expert systems

Laurent

1987

The Knowledge Engineering Review

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The concept of an expert system covers an increasingly large group of software packages which often have more dissimilarities than points in common. We shall not attempt to give a precise definition of an expert system here, because this might impose too restrictive a framework on the rest of our discussion. We shall simply state that, as is generally recognized, an expert system is a piece of software intended to resolve a certain category of problems, that it uses for this purpose a large quantity of knowledge specific to the field in question, and that in each expert system there is a very distinct separation between this knowledge and the procedures which make use of it.

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“…When chess has been used as a test bed (F1ann and Dietterich, 1989;Minton, 1984;Pitrat, 1977;Quinlan, 1983;Wilkins, 1982) only a small sub-domain of the game was used, so that fundamental efficiency issues that AI must grapple with have been largely unaddressed. However, we feel that there is a third approach that relies neither on search nor on the symbolic computation approach of knowledge-oriented AI.…”

Section: Robert Levinsonmentioning

confidence: 99%