Ricardo Shirota Filho

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Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilis-tic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine the computation of Γ-maximin policies for MDPIPs using(More)
This paper presents new insights and novel algorithms for strategy selection in sequential decision making with partially ordered preferences; that is, where some strategies may be incomparable with respect to expected utility. We assume that incomparability amongst strategies is caused by indeterminacy/imprecision in probability values. We investigate six(More)
Previous algorithms that generate policies in decision trees with imprecise probabilities [1, 7] employ multilinear programming to compute expected values for policies, that is, a program where the objective function involves a summation of products of variables, and the constraints are linear functions defining the set of probabilities. Despite being a(More)
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