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 offered including 12 10-page posters have been rigorously reviewed and chosen from greater than 70 submissions. The papers are geared up in topical sections on video games and constraint delight; ordinary language processing; wisdom illustration; AI purposes; desktop studying and knowledge mining; making plans, theorem proving, and synthetic existence; and neural networks.
Read Online or Download 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 PDF
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Additional resources 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
6] R. Dechter and J. Pearl. Tree clustering for constraint networks. Artificial Intelligence, 38:353–366, 1989.  E. C. Freuder and R. J. Wallace. Partial constraint satisfaction. Artificial Intelligence, 58(1-3):21–70, 1992.  Georg Gottlob, Nicola Leone, and Fransesco Scarcello. A comparison of structural decomposition methods. In Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, pages 394–399, 1999. A Constraint Directed Model for Partial Constraint Satisfaction Problems 39  M.
Consider an extension 36 Sivakumar Nagarajan, Scott Goodwin, and Abdul Sattar to this tuple, tupnew =tupi 1 tupi+1 . This extension is an assignment on a variable set Vtupnew = Vti ∪ Vtupi+1 . By definition of the par-consistent extension, for all variables in Vti ∩ Vtupi+1 , tupnew [Vti ∩ Vtupi+1 ] = tupi [Vti ∩ Vtupi+1 ]. , all previous assignments to variables remain unchanged, and hence all constraints that were previously satisfied remain 2 satisfied in the par-consistent extension. ∼ Definition 8.
N ewSol := CurrentSol 1 CurrT up 8. CESNewSol := Constraints completely instantiated by N ewSol 9. Foreach New Constraint Ci in CESNewSol 10. if N ewSol is consistent with Ci and CDBT(N ewSol, Cons-CESNewSol , T uplesConsnext ) = ‘‘finished’’ then return ‘‘finished’’; 11. else // Otherwise back up, to try a different value 12. return CDBT(CurrSol, Cons,T uples-CurrT up) end Fig. 1. Revised CDBT algorithm step of the algorithm CDBT removes constraints that are verified against from Cons. Cons therefore represents the set of constraints that have not yet been satisfied by the current instantiation.