Advances in Artificial Intelligence: 13th Biennial by Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton

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By Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton (eds.)

This publication constitutes the refereed court cases of the thirteenth Biennial convention of the Canadian Society for Computational experiences of Intelligence, AI 2000, held in Montreal, Quebec, Canada, in may perhaps 2000. The 25 revised complete papers provided including 12 10-page posters have been conscientiously reviewed and chosen from greater than 70 submissions. The papers are geared up in topical sections on video games and constraint delight; average language processing; wisdom illustration; AI functions; computer studying and information mining; making plans, theorem proving, and synthetic existence; and neural networks.

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Extra info for Advances in Artificial Intelligence: 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000 Montéal, Quebec, Canada, May 14–17, 2000 Proceedings

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Definition 2. If Vi and Vj are sets of variables, let Si be an instantiation of the variables in Vi . Si [Vj ] is the tuple consisting of only the components of Si that correspond to the variables in Vj . This is also called the projection of tuple Si on the variables in Vj . Let Ci , Cj be two constraints ∈ C. The join of Ci , Cj , denoted by Ci 1 Cj = Cij , is the set {t | t ∈ Sij ∧ (t[Vi ] ∈ Si ) ∧ (t[Vj ] ∈ Sj )}. The set of all solutions of a constraint satisfaction problem, is equal to the join of the relational instances corresponding to the constraints [2, 9].

Hsu. Search Control Methods in Deep Blue. AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information, pages 19–23. AAAI Press, 1999. [Don93] C. Donninger. Null Move and Deep Search: Selective Search Heuristics for Obtuse Chess Programs. Journal of the International Computer Chess Association, 16(3):137–143, 1993. [ET76] S. Even and R. E. Tarjan. A combinatorial problem which is complete in polynomial space. Journal of the Association for Computing Machinery, 23:710–719, 1976.

The point to be noted is that the algorithm backtracks when the number of inconsistencies is increased above the best complete solution that has been recorded so far. BTBB visits 14 nodes in all, performs 15 constraint checks and finds a solution that violates 1 constraint as the best solution in terms of least constraint violations. As before, search concludes when a satisfactory solution is found, or all choices for future values for variables are exhausted. Nodes with a dashed circle around them denote inconsistent nodes, whose level of inconsistency is acceptable.

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