Download e-book for iPad: Advances in Artificial Intelligence: 19th Conference of the by Luc Lamontagne, Mario Marchand

By Luc Lamontagne, Mario Marchand

ISBN-10: 3540346287

ISBN-13: 9783540346289

This booklet constitutes the refereed complaints of the nineteenth convention of

the Canadian Society for  Computational experiences of Intelligence, Canadian

AI 2006, held in Québec urban, Québec, Canada in June 2006.

The forty seven revised complete papers provided have been conscientiously reviewed and chosen

from 220 submissions. The papers are equipped in topical sections on

agents, bioinformatics, constraint delight and allotted seek,

knowledge illustration and reasoning, ordinary language, reinforcement

learning and, supervised and unsupervised learning.

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Extra info for Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City,

Sample text

1 Introduction Recent developments in information technology and increasing problems in the health field, including population ageing and medical staff shortages, have opened the way to a whole set of new and promising research avenues, most notably, work on smart homes. A growing literature [3][6][8][11] has explored the process by which cognitive assistance, inside a smart home, is provided to occupants suffering from cognitive deficiencies such as Alzheimer’s disease and schizophrenia, for the performance of their Activities of Daily Living (ADL).

Among all Sj s, the most profitable agents S ∗ are those that provide the highest additional profit υ ∗ and are kept as the base for the further shrinking coalitions. The coalition keeps shrinking in this fashion until there are no prunable members left in T G . Then the next profitable sibling of the base Sj will be the new base. This repetition goes on until it cannot find the new base. The number of coalitions each agent ak has to maintain is also much smaller compared to that of the exhaustive search.

Sandholm et. al. [3] study the problem of coalition structure generation. Since the number of coalition structures can be very large for exhaustive search, they argue whether the optimal coalition structure found via a partial search can be guaranteed to be within a bound from optimum. They propose an anytime algorithm that establishes a tight bound withing a minimal amount of search. 6 Conclusion and Future Work We propose an algorithm for computing optimal coalition structure for linear programming domains among fully-cooperative agents.

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Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City, by Luc Lamontagne, Mario Marchand


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