Artificial Intelligence: Methodology, Systems, and by Jérôme Euzenat, John Domingue

By Jérôme Euzenat, John Domingue

This ebook constitutes the refereed lawsuits of the twelfth foreign convention on man made Intelligence: method, structures, and functions, AIMSA 2006, held in Varna, Bulgaria in September 2006.

The 28 revised complete papers provided including the abstracts of two invited lectures have been rigorously reviewed and chosen from eighty one submissions. The papers are geared up in topical sections on brokers, constraints and optimization, consumer issues, selection help, types and ontologies, computing device studying, ontology manipulation, usual language processing, and applications.

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Extra resources for Artificial Intelligence: Methodology, Systems, and Applications: 12th International Conference, AIMSA 2006, Varna, Bulgaria, September 12-15, 2006, Proceedings

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Fallacies. Methuen, London, 1970. 3. J. MacKenzie. Question-begging in non-cumulative systems. Journal of Philosophical Logic, 8:117–133, 1979. 4. S. Parsons, M. Wooldridge, and L. Amgoud. On the outcomes of formal inter-agent dialogues. In Proc. AAMAS’03, pages 616–623, Melbourne, 2003. 5. H. Prakken. Relating protocols for dynamic dispute with logics for defeasible argumentation. Synthese, 127:187–219, 2001. 6. E. Alonso. A formal framework for the representation of negotiation protocols. Inteligencia Artificial, 3/97:30–49, 1997.

14. 15. 16. 17. 18. 19. : Reasoning about emotional agents. : The cognitive structure of emotions. : C&L intention revisited. : Modal probability, belief, and actions. : The expression of emotions in man and animals. : An argument for basic emotions. : How shall an emotion be called? : Circumplex models of personality and emotions. : Simulating the emotion dynamics of a multimodal conversational agent. : Emotion and Adaptation. : The emotions. : A domain-independent framework for modeling emotion.

Our approach is different, it consists in providing a general Boolean form including both CNF (SAT) and CSP representations, rather than translating CSPs into SAT forms. We describe in this section, a new Boolean encoding which generalizes the CNF formulation, and show how n-ary CSPs are naturally represented in an optimal way (no overhead in size in comparison to the CSP representation) in this encoding. 1 The Generalized Normal Form (GNF) A generalized clause C is a disjunction of Boolean formulas f1 ∨.

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