By Adam Milstein (auth.), Balázs Kégl, Guy Lapalme (eds.)
The 18th convention of the Canadian Society for the Computational examine of Intelligence (CSCSI) persisted the good fortune of its predecessors. This set of - pers re?ects the variety of the Canadian AI group and its foreign companions. AI 2005 attracted a hundred thirty five top of the range submissions: sixty four from Canada and seventy one from all over the world. of those, 8 have been written in French. All submitted papers have been completely reviewed by way of a minimum of 3 individuals of this system Committee. a complete of 30 contributions, accredited as lengthy papers, and 19 as brief papers are integrated during this quantity. We invited 3 amazing researchers to offer talks approximately their present study pursuits: Eric Brill from Microsoft study, Craig Boutilier from the college of Toronto, and Henry Krautz from the college of Washington. The association of this kind of winning convention bene?ted from the coll- oration of a lot of people. most appropriate, we want to specific our apprec- tion to this system Committee participants and exterior referees, who supplied well timed and signi?cant experiences. to regulate the submission and reviewing approach we used the Paperdyne approach, which used to be built through Dirk Peters. We owe specified because of Kellogg sales space and Tricia d’Entremont for dealing with the neighborhood arrangementsandregistration.WealsothankBruceSpencerandmembersofthe CSCSI government for all their e?orts in making AI 2005 a winning conference.
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Extra resources for Advances in Artificial Intelligence: 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, Canada, May 9-11, 2005. Proceedings
While not the end of nogood set do 4. if temp degreeU nsat > threshold and 5. temp degreeU nsat < Tmin 6. then Tmin = temp degreeU nsat; 7. temp degreeU nsat = degreeU nsat from next nogood; 8. threshold = Tmin ; In a realistic problem, diﬀerent agents may arrive at diﬀerent values for their local degreeU nsat, meaning that diﬀerent levels of optimization can be reached by diﬀerent agents. Finally, the global degreeU nsat can be used to measure the degree of relaxation for the entire problem. 5 Experimental Results We evaluated our algorithm on a benchmark set of 3-colouring problems, using the problem generator described in .
Each agent has a degreeU nsat threshold it is trying to attain, and is free to relax any constraint to attain that threshold, each time it gets a turn. Example: To further clarify the details of the algorithm, we use a distributed 3colouring problem shown in Figure 1. The goal of the problem is to assign colours to each node so that nodes connected by the same arc have diﬀerent colours2 . In Figure 1 (a), Agent 1 has two intra-agent constraints and Agent 2 has three intraagent constraints, and they both have four inter-agent constraints.
S. Minton, M. D. Johnston, A. B. Philips, and P. Laird. Minimizing conﬂicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artiﬁcial Intelligence, pages 161–205, 1992. 6. Pragnesh Jay Modi, Wei-Min Shen, Milind Tambe, and Makoto Yokoo. An asynchronous complete method for distributed constraint optimization. In Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pages 161–168. ACM Press, 2003. 7. Makoto Yokoo. Constraint relaxation in distributed constraint satisfaction problem.