By Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton (eds.)
This e-book constitutes the refereed court cases of the thirteenth Biennial convention of the Canadian Society for Computational experiences 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; usual 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 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
Cm }. Each Ci ∈ Ccover is given as Vi , Si , where Vi ⊆ V . Ccover covers V iff m i=1 Vi =V . Ccover is a constraint cover of V . As well, Ccover is a minimal constraint cover of V if it is a constraint cover of V and no proper subset of Ccover is a constraint cover of V . The algorithm has a simple recursive structure similar to BT. Figure 1 describes a revised version of constraint directed backtracking. CDBT finds a constraint cover from the given set of constraints that covers all the variables of the CSP.
6 Analysis of CDBB If Ccover is a minimal constraint cover, |Ccover | ≤ |V |. This can be seen by the fact that while selecting constraints to form the cover, each additional constraint, covers at least one new variable, and hence all that is required is a cover of size of at most |V |. 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.
CDBT with a conservative constraint selection strategy can be shown to always have a smaller search space than BT but often a more aggressive heuristic can perform better on average, although no worst case performance guarantees can be provided. 1 Initially Cons represents the set of all constraints, while T uples represents the set of values (tuples) in the domain of the currently selected constraint. At each step the selected constraint is joined with the previously constructed partial solution.