Advances in Artificial Intelligence: 19th Conference of the by Michael Y. K. Cheng, Robin Cohen (auth.), Luc Lamontagne,

By Michael Y. K. Cheng, Robin Cohen (auth.), Luc Lamontagne, Mario Marchand (eds.)

This publication constitutes the refereed complaints of the nineteenth convention of the Canadian Society for Computational reviews of Intelligence, Canadian AI 2006, held in Qu?bec urban, Qu?bec, Canada in June 2006.

The booklet provides forty seven revised complete papers, conscientiously reviewed and chosen from 220 submissions. The papers are prepared in topical sections on brokers, bioinformatics, constraint pride and disbursed seek, wisdom illustration and reasoning, average language, reinforcement studying and, supervised and unsupervised studying. comprises CD-ROM.

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Extra resources for Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Québec City, Québec, Canada, June 7-9, 2006. Proceedings

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The answer is that the combination of the incomparable possible plans is not done blindly. First, we only consider the consistent possible plans, which satisfy the stability and closure criteria (first filter). Second, the possible plans that we consider are those which are in the lattice structure bounded by the upper and lower bounds (second filter). Finally, for each pair of incomparable plausible plans, we combine them by using the disunification operation (third filter), which will be defined in the next section.

In: Proceedings of the first international joint conference on Autonomous agents and multiagent systems, ACM Press (2002) 1062–1063 7. : Task allocation via coalition formation among autonomous agents. In: Proc. of IJCAI. (1995) 655–661 8. : Generating coalition structures with finite bound from the optimal guarantees. In: Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2 (AAMAS’04), pp. 564-571. (2004) 9. : A distributed algorithm for coalition formation in linear production domain.

However, the main research work that investigated the automatic map generation process using multiagent systems was the AGENT2 project. The general approach used in this project [6,11] consisted in transforming the geographic objects contained in the database into agents. The goals of these agents are to satisfy their cartographic constraints as much as possible. They choose and apply transformations which are adapted to the current configuration of space. They assess and control the evolution of their states with respect to performed actions.

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