By Adam Milstein (auth.), Balázs Kégl, Guy Lapalme (eds.)
The 18th convention of the Canadian Society for the Computational learn of Intelligence (CSCSI) persisted the good fortune of its predecessors. This set of - pers re?ects the variety of the Canadian AI neighborhood and its foreign companions. AI 2005 attracted a hundred thirty five top 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 via at the least 3 contributors of this system Committee. a complete of 30 contributions, approved as lengthy papers, and 19 as brief papers are incorporated during this quantity. We invited 3 individual researchers to offer talks approximately their present study pursuits: Eric Brill from Microsoft examine, Craig Boutilier from the collage of Toronto, and Henry Krautz from the collage of Washington. The association of any such profitable convention bene?ted from the coll- oration of a lot of persons. most excellent, we wish to precise our apprec- tion to this system Committee individuals and exterior referees, who supplied well timed and signi?cant experiences. to control the submission and reviewing strategy we used the Paperdyne approach, which used to be built through Dirk Peters. We owe unique because of Kellogg sales space and Tricia d’Entremont for dealing with the neighborhood arrangementsandregistration.WealsothankBruceSpencerandmembersofthe CSCSI govt for all their e?orts in making AI 2005 a winning conference.
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Additional 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
2) Communication/Observational actions. Actions such as communication and observing squares in other areas of the world fall into this group. With the execution of these actions the agents have to deal with things such as communication delays and distortion of information. 2 World Rules, Environment Features, and Their Connection to Multi-agent Systems As already stated, a major goal in the development of ARES and ARES 2 was to provide a lot of possibilities of how to conﬁgure the environment to allow for different foci on the general problems of multi-agent systems.
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The threshold is not achievable) the threshold value is increased by an amount that depends on the related constraints. By iteratively applying this method the optimal degreeU nsat solution will eventually be found. If we are uncertain as to whether a problem is over-constrained, setting the initial threshold to zero will guarantee ﬁnding the optimal solution. Otherwise, setting the threshold at a reasonable lower bound on the optimal solution threshold will reduce the amount of processing required.
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 by Adam Milstein (auth.), Balázs Kégl, Guy Lapalme (eds.)