Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 6th European Confere Salem Benferhat,Philippe Besnard Conference procee

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Value Iteration over Belief Subspacell-known algorithm for solving POMDPs. It is notoriously difficult because at each step it needs to account for every belief state in a continuous space. In this paper, we show that value iteration can be conducted over a subset of belief space. Then, we study a class of POMDPs, namely informative P

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Troubleshooting with Simultaneous Modelsis complex then it is convenient to create several Bayesian Networks, each designed to solve a particular problem. At the beginning of a troubleshooting process, it is often necessary to help the user to select the proper model. Complications arise if the user is able to give only a vague descriptio

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An Operational View of Coherent Conditional Previsionsure is the direct applicability of the results to model practical problems. In fact the check of coherence and the inferential steps are reduced to the solvability of linear systems and of linear programming problems, respectively. The guideline of the procedure has been the already stated results f

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Importance Sampling in Bayesian Networks Using Antithetic Variablesnce sampling is that during the simulation, configurations are obtained using antithetic variables (variables with negative correlation), achieving a reduction of the variance of the estimation. The performance of the new algorithm is tested by means of some experiments carried out over four large r

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