Titlebook: Artificial Intelligence: Methodology, Systems, and Applications; 12th International C Jér?me Euzenat,John Domingue Conference proceedings 2

團(tuán)結(jié)

https://doi.org/10.1007/978-3-319-26252-9bsystem for color descriptions will certainly facilitate web applications in the domains concerned, such as image retrieval, clothing search, etc. However, both keyword matching and semantic mediation by the current ontologies are confronted with difficulties in precisely evaluating the similarity b

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Applications: A Variety of Circuits,approach towards their representation. We propose an argumentation ontology, called “Argontonaut,” which has been developed for the domain of standards-driven W3C mailing lists. We use the extensible nature of RDF to fuse an argumentation-based approach with one grounded in an issue-management syste

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Mohammed Larouz,Soufiane Abouabdelkaderty is connected to uncertainty by nature, and these standard algorithms of clustering do not deal with this uncertainty pervaded in their parameters. In this paper we develop a clustering method in an uncertain context based on the K-modes method and the belief function theory. This so-called belief

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https://doi.org/10.1007/11861461Artificial intelligence; agents; intelligence; learning; machine learning; ontology; optimization

corpus-callosum

978-3-540-40930-4Springer-Verlag Berlin Heidelberg 2006

繼而發(fā)生

Feedback and Control for Everyonehem restrain their agents to a few emotions among these twenty-two ones, and are more or less faithful to their definition. In this paper we propose to extend standard BDI (belief, desire, intention) logics to account for more emotions while trying to respect their definitions as exactly as possible.

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Feedback und leistungsbezogene Variablen,tabase. In this paper we define a framework where sampling with and without replacement can be viewed as the extreme cases of a more general process, and analyze the performance of the extension of bagging to such framework.

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