By Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton (eds.)
This publication constitutes the refereed lawsuits of the thirteenth Biennial convention of the Canadian Society for Computational reviews of Intelligence, AI 2000, held in Montreal, Quebec, Canada, in might 2000. The 25 revised complete papers offered including 12 10-page posters have been rigorously reviewed and chosen from greater than 70 submissions. The papers are equipped in topical sections on video games and constraint pride; typical language processing; wisdom illustration; AI purposes; desktop studying and knowledge mining; making plans, theorem proving, and synthetic lifestyles; 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
It can take many plies of search to uncover these connections implicitly. It seems obvious to humans that knowledge about these patterns is therefore a key ingredient to any strong Hex player. Queenbee is equipped to recognize many common patterns, but it is not yet clear how to integrate this knowledge into the evaluation function. Worse still, the patterns cause significant horizon-type problems7 that actually lead to a net decrease in playing strength. For this reason, Queenbee currently does not use the patterns at all.
Also CDBB can be applied to real world over-constrained domains and its performance compared with successful local search methods. References  Fahiem Bacchus and Peter van Beek. On the conversion between non-binary and binary constraint satisfaction problems. In Proceedings of the 15th National Conference on Artificial Intelligence, pages 311–318, 1998.  W. Bibel. Constraint satisfaction from a deductive viewpoint. Artificial Intelligence, 35:401–413, 1988.  M. Boddy and D. L. Dean. Solving time dependent planning problems.
As before CDBB selects constraints C1 and C2 to form the cover. It visits 4 nodes in all and performs 2 constraint checks. As before, if a different set of constraints is selected to form the cover, CDBB visits a different number of nodes each time. It turns out that if C1 and C3 are selected, CDBB visits 3 nodes in all, and if C2 C3 are selected it visits 6 nodes in all. The algorithm finds a similar solution that violates 1 constraint. 6 Analysis of CDBB If Ccover is a minimal constraint cover, |Ccover | ≤ |V |.
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 by Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton (eds.)