By Daniele Nardi, Riccardo Rosati (auth.), Evelina Lamma, Paola Mello (eds.)
This booklet includes the prolonged models of 33 papers chosen between these initially provided on the 6th Congress of the Italian organization for man made Intelligence (AI*IA). The congress of the AI*IA is the main appropriate Italian occasion within the box of synthetic Intelligence, and has been receiving a lot cognizance from many researchers and practitioners of other international locations. The 6th congress used to be held in Bologna, 14-17 September 1999, and used to be prepared in twelve medical periods and one demo consultation. The papers right here accrued file on major paintings performed in numerous parts of man-made intelligence, in Italy and different international locations. parts reminiscent of automatic reasoning, wisdom illustration, making plans, and computer studying remain completely investigated. the gathering additionally indicates a turning out to be curiosity within the box of multi-agent structures, conception and robotics, and temporal reasoning. many of us contributed in several how you can the luck of the congress and to this quantity. firstly, the participants 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 e-book during this quantity. They supplied 3 reports for every manuscript, by way of counting on the help of priceless extra reviewers. The individuals 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 helping at fixing difficulties in the course of and after the congress.
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Extra info for AI*IA 99: Advances in Artificial Intelligence: 6th Congress of Italian Association for Artificial Intelligence Bologna, Italy, September 14–17, 1999 Selected Papers
Informally speaking, the more a belief network’s proba- Sensitivity Analysis for Threshold Decision 39 bility assessments can be varied, the more robust the decision based upon the network is. The paper is organised as follows. In Section 2, we brieﬂy review the formalism of Bayesian belief networks. In Section 3, we outline the threshold model for decision making. In Section 4, we detail the basic method of sensitivity analysis and its enhancement for threshold decision making. The paper ends with some conclusions and directions for further research in Section 5.
2. 4 Practical Signiﬁcance Above we have seen that important Bayesian network models can be mapped to fragments of the certainty-factor model. However, the results of this paper would have little signiﬁcance when in almost all practical network models the assumptions underlying decomposable, causal independence would not be satisﬁed. But the opposite appears to be the case: in many practical Bayesian network models, a lot of causal independence assumptions are made. This is to be expected, because the technology of Bayesian networks is only practically useful when a large amount of information concerning independence, with causal independence as a special case, is available in a domain.
H. 8 1 (b) Fig. 4. A sensitivity analysis of the example belief network; the eﬀects of varying the assessments for the probabilities p(mc), (a), and p(b | ¬mc), (b), on the prior probability of disease Pr(b) are shown. show diﬀerent sensitivities. To reveal these, a sensitivity analysis can be performed with regard to a posterior probability of interest. Such an analysis allows for investigating the robustness of the network’s output for speciﬁc cases or for case proﬁles. We once again perform a sensitivity analysis of our example belief network, this time taking for the probability of interest the posterior probability Pr(b | sh) of the presence of a brain tumour in a patient who is known to suﬀer from severe headaches.