By Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton (eds.)
This e-book constitutes the refereed complaints of the thirteenth Biennial convention of the Canadian Society for Computational stories 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 pride; ordinary language processing; wisdom illustration; AI purposes; computer studying and knowledge mining; making plans, theorem proving, and synthetic existence; 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
Cell potentials the lowest W-potential that occurs on the board. In the example of Figure 4 the white board potential is 4, and the black board potential is 5. As lower potentials are better, it appears that White is ahead. In the same figure, it can be seen that both Black and White have only one cell that actually realizes their board potential. It would be better to have more than one cell that realizes the board potential, so as to have more attack options and be less vulnerable to the opponent blocking the chain.
In section 4 we illustrate the execution of the algorithm on a simple example. Section 5 describes the partial constraint satisfaction model and the CDBB algorithm which is a structure driven algorithm based on the 28 Sivakumar Nagarajan, Scott Goodwin, and Abdul Sattar dual constraint graph. Our experimental results (section 7) indicate that CDBB consistently finds better solutions much more quickly than BTBB (backtracking with branch and bound). 2 Preliminary Definitions Let V, D, C be a CSP where V is the set of variables, D are their domains, and C is the set of constraints.
It should also be noted that these are all winning positions at the beginning of the game, and as such they are the most difficult of all 6 × 6 Hex problems. 6 Work in Progress The weights for the move categories are hand picked, based on intuitions about the move categories and their relation to the game. These weights form an attractive target for machine learning techniques. Experiments are currently running to apply the “Learning Search Control” algorithm of Yngvi Bj¨ ornsson [Bj¨ o00] to dynamically establish these weights based on actual game play.
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.)