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

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

This ebook constitutes the refereed lawsuits of the thirteenth Biennial convention of the Canadian Society for Computational reports 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 rigorously reviewed and chosen from greater than 70 submissions. The papers are equipped in topical sections on video games and constraint pride; common language processing; wisdom illustration; AI purposes; computer studying and information mining; making plans, theorem proving, and synthetic lifestyles; and neural networks.

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Additional 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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Our algorithm can be extended with intelligent backtracking schemes and local consistency maintenance mechanisms just like backtracking has been in the past. 1 Introduction Solving a constraint satisfaction problem (CSP) involves finding a consistent assignment of values to variables subject to restrictions on the combinations of values. If the problem is over-constrained it has no solution. Partial constraint satisfaction problems (PCSP) were proposed by Freuder and Wallace [7], to represent and solve relaxed versions of over-constrained problems.

Gal86] D. Gale. The Game of Hex and the Brouwer Fixed Point Theorem. American Mathematical Monthly, pages 818–827, 1986. [LBT89] D. Levy, D. Broughton, and M. Taylor. The Sex Algorithm in Computer Chess. Journal of the International Computer Chess Association, 12(1):10– 21, 1989. [Mar86] T. Marsland. A Review of Game-Tree Pruning. Journal of the International Computer Chess Association, 9(1):3–19, 1986. au Abstract. e. problems may be over-constrained). In such cases, we want a partial solution that satisfies as many constraints as possible.

If the level of inconsistency of a node is ninc , then the level of inconsistency of all of its child nodes is ≥ ninc . Proof: The proof follows from Definitions 6 and 7. Extensions cannot remove 2 inconsistencies. Lemma 2. At every level, any constraint that is satisfied at this level, will remain satisfied by all of its child nodes. Proof: The algorithm attempts to construct a par-consistent extension of a tupi . tupi is a partial assignment on a variable set Vti . Consider an extension 36 Sivakumar Nagarajan, Scott Goodwin, and Abdul Sattar to this tuple, tupnew =tupi 1 tupi+1 .

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