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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 e-book constitutes the refereed lawsuits of the thirteenth Biennial convention of the Canadian Society for Computational experiences of Intelligence, AI 2000, held in Montreal, Quebec, Canada, in may well 2000. The 25 revised complete papers awarded including 12 10-page posters have been conscientiously reviewed and chosen from greater than 70 submissions. The papers are prepared in topical sections on video games and constraint delight; typical language processing; wisdom illustration; AI functions; computing device studying and information mining; making plans, theorem proving, and synthetic existence; and neural networks.

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Although the size of a minimal constraint cover is upper bounded by |V |, in practice in CSPs of higher arities, this number is even less. In fact, if Ccover is a minimal constraint cover of a CSP of arity k, |Ccover | ≤ n-k+1. This again follows trivially from the fact that a CSP of arity k has at least one constraint of arity k. Including this constraint in a minimal constraint cover Ccover covers k variables. This leaves n-k variables to be covered. In the worst case, n-k constraints are required to cover these variables.

Constraint directed backtracking. In A. Sattar, editor, Advanced Topics in AI, pages 47–56. Springer Verlag, 1997. Volume 1342, LNAI. [13] W. Pang and S. Goodwin. Characterizing tractable csps. In Proceedings of AI’98, the Twelfth Canadian Conference on Artificial Intelligence, pages 259–272, 1998. [14] F. Rossi, C. Petrie, and V. Dhar. On the equivalence of constraint satisfaction problems. In Proceedings of the 9th European Conference on Artificial Intelligence, pages 550–556, 1990. [15] J. Thornton and A.

When computational resources are bounded, the algorithm can return partial solutions in an anytime fashion. Some interesting characteristics of the proposed algorithm are discussed. The algorithm is implemented and tested on a set of randomly generated problems. Our experimental results demonstrate that the CDBB consistently finds better solutions more quickly than backtracking with branch and bound. Our algorithm can be extended with intelligent backtracking schemes and local consistency maintenance mechanisms just like backtracking has been in the past.

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