By Adam Milstein (auth.), Balázs Kégl, Guy Lapalme (eds.)
The 18th convention of the Canadian Society for the Computational research of Intelligence (CSCSI) persevered the good fortune of its predecessors. This set of - pers re?ects the variety of the Canadian AI group and its overseas companions. AI 2005 attracted a hundred thirty five top of the range submissions: sixty four from Canada and seventy one from world wide. of those, 8 have been written in French. All submitted papers have been completely reviewed via not less than 3 participants of this system Committee. a complete of 30 contributions, authorised as lengthy papers, and 19 as brief papers are integrated during this quantity. We invited 3 amazing researchers to offer talks approximately their present examine pursuits: Eric Brill from Microsoft examine, Craig Boutilier from the college of Toronto, and Henry Krautz from the college of Washington. The association of one of these profitable convention bene?ted from the coll- oration of many people. premier, we wish to precise our apprec- tion to this system Committee individuals 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 constructed via Dirk Peters. We owe distinctive due to 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 profitable 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
4. The agent calculates the value of degreeU nsat. If degreeU nsat ≤ threshold, the agent sends messages to neighbouring agents. Each message contains the degreeU nsat value and the local instantiation of the agent. Otherwise, we go back to 3; 5. The search will stop when each agent i detects that its degreeU nsati ≤ thresholdi . In eﬀect, ODAO is using the change in degreeU nsat as a distance measure, and so is implicitly placing an importance value on each constraint. , when a relaxation needs to occur, the constraint that causes the smallest change in degreeU nsat is selected.
Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources, Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 03), 2003 14. , Olsen, T. : Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data, Proceedings of the Fourth IEEE International Conference on Data Mining(ICDM 04), pp. 67-74, 2004 15. Quinlan, J. : Induction of Decision Trees, Machine Learning, Vol. 1, pp. K. g. [2, 6]). What is lacking, however, is a method for integrating these individual adjustable autonomy algorithms into a cohesive solution for the delegation of tasks for the society.
AAAI Press. ARES 2: A Tool for Evaluating Cooperative and Competitive Multi-agent Systems Jörg Denzinger and Jordan Kidney Dep. ca Abstract. The Agent Rescue Emergency Simulator (ARES) system provides a simpliﬁed rescue scenario similar to Robocup Rescue for use in the educational or research ﬁelds when evaluating multi-agent systems. The environment within ARES abstracts ideas down to key features while still allowing for a wide range of scenarios requiring different skills to be presented to the agents.
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.)