AI*IA 99: Advances in Artificial Intelligence: 6th Congress by Daniele Nardi, Riccardo Rosati (auth.), Evelina Lamma, Paola

By Daniele Nardi, Riccardo Rosati (auth.), Evelina Lamma, Paola Mello (eds.)

This e-book includes the prolonged models of 33 papers chosen between these initially awarded on the 6th Congress of the Italian organization for man made Intelligence (AI*IA). The congress of the AI*IA is the main correct Italian occasion within the box of synthetic Intelligence, and has been receiving a lot consciousness from many researchers and practitioners of other nations. The 6th congress was once held in Bologna, 14-17 September 1999, and was once equipped in twelve medical classes and one demo consultation. The papers right here accumulated record on major paintings conducted in several components of man-made intelligence, in Italy and different international locations. components similar to computerized reasoning, wisdom illustration, making plans, and computing device studying remain completely investigated. the gathering additionally indicates a transforming into curiosity within the box of multi-agent structures, conception and robotics, and temporal reasoning. many folks contributed in several how you can the good fortune of the congress and to this quantity. to begin with, the individuals of this system committee who successfully dealt with the reviewing of the sixty four papers submitted to the congress, and in a while the reviewing of the forty-one papers submitted for book during this quantity. They supplied 3 stories for every manuscript, by means of hoping on the help of worthy extra reviewers. The contributors of the organizing committee, specifically Rosangela Barruffi, Paolo Bellavista, Anna Ciampolini, Marco Cremonini, Enrico Denti, Marco Gavanelli, Mauro Gaspari, Michela Milano, Rebecca Montanari, Andrea Omicini, Fabrizio Riguzzi, Cesare Stefanelli, and Paolo Torroni, labored hardy aiding at fixing difficulties in the course of and after the congress.

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In )=e k=1 Based on the assumptions above, and using marginalisation and conditioning, it also holds that n Pr(e|C1 , . . , Cn ) = Pr(Ik |Ck ) Pr(e|I1 , . . ,In (6) k=1 Formula (5) above is practically spoken still not very useful, because the size of the specification of the function f is exponential in the number of its arguments. The resulting probability distribution is therefore in general computationally intractable, both in terms of space and time requirements. An important subclass Certainty-Factor-Like Structures in Bayesian Networks A 31 B C Fig.

Catania. A constraint-based approach to shape management in multimedia databases. Multimedia Systems, 6:2-16, 1998. 3. S. S. Fu. Query by pictorial example. IEEE Trans. Software Engineering, 6(6), 1980. 4. K. Chang and et al. Intelligent image database system. IEEE Trans. Software Engineering, 14(5), 1988. 5. E. Di Sciascio and M. Mongiello. Query by sketch and relevance feedback for content-based image retrieval over the web. Journal of Visual Languages and Computing, 10(6), 1999. 6. E. Ardizzone et al.

A belief network therefore allows for the computation of any probability pertaining to its variables. For this purpose, various algorithms are available, that provide for computing probabilities of interest and for processing evidence, that is, for entering observations into the network and subsequently computing the revised probability distribution given these observations [Pea88,LS88]. The details of these algorithms are not relevant for the present paper. 3 Threshold Decision Making In the medical domain, Bayesian belief networks are often used for diagnostic purposes.

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