By Adam Milstein (auth.), Balázs Kégl, Guy Lapalme (eds.)
The 18th convention of the Canadian Society for the Computational research of Intelligence (CSCSI) endured the good fortune of its predecessors. This set of - pers re?ects the range of the Canadian AI group and its overseas companions. AI 2005 attracted a hundred thirty five fine quality 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 means of not less than 3 individuals of this system Committee. a complete of 30 contributions, authorized as lengthy papers, and 19 as brief papers are incorporated during this quantity. We invited 3 unusual researchers to offer talks approximately their present study pursuits: Eric Brill from Microsoft study, Craig Boutilier from the collage of Toronto, and Henry Krautz from the college of Washington. The association of any such profitable convention bene?ted from the coll- oration of lots of individuals. top-rated, we want to specific our apprec- tion to this system Committee participants and exterior referees, who supplied well timed and signi?cant reports. to regulate the submission and reviewing technique we used the Paperdyne method, which used to be built by way of 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 info 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
Eugene C. Freuder and Richard J. Wallace. Partial constraint satisfaction. Artiﬁcial Intelligence, 58(1-3):21–70, 1992. 2. Katsutoshi Hirayama and Makoto Yokoo. Distributed partial constraint satisfaction problem. In Proceedings of the Third International Conference on Principles and Practice of Constraint Programming (CP-97), pages 222–236, 1997. 3. Katsutoshi Hirayama and Makoto Yokoo. An approach to over-constrained distributed constraint satisfaction problems: Distributed hierarchical constraint satisfact.
Most of these approaches are agent-based high level learning such as meta-learning , knowledge probing , B. Kégl and G. ): AI 2005, LNAI 3501, pp. 25 – 32, 2005. © Springer-Verlag Berlin Heidelberg 2005 26 S. Baik, J. Cho, and J. Bala and mixture of experts , Bayesian model averaging , and stacked generalization . However, these approaches still only have the ability to estimate a global data model through the aggregation of the local results, rather than generating an exact correct global model.
Pynadath, and M. Tambe. Adjustable Autonomy for the Real World. In Agent Autonomy. Kluwer Publishers, 2004. 7. D. Schreckenghost, C. Martin, P. Bonasso, D. Kortenkamp, T. Miliam, and C. Thronesbery. Supporting Group Interaction Among Humans and Autonomous Agents. In Proceedings of the AAAI2002 Workshop on Autonomy, Delegation, and Control: From Inter-agent to Groups, pages 72–77, Menlo Park, CA, 2002. AAAI Press. ARES 2: A Tool for Evaluating Cooperative and Competitive Multi-agent Systems Jörg Denzinger and Jordan Kidney Dep.