Evaluating interval-valued influence diagrams

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Evaluating interval-valued influence diagrams

Influence diagrams are probabilistic graphical models used to represent and solve sequential decision problems under uncertainty. Sharp numerical values are required to quantify probabilities and utilities. This might be an issue with real models, whose parameters are typically obtained from expert judgements or partially reliable data. We consider an interval-valued quantification of the param...

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Variable Elimination for Interval-Valued Influence Diagrams

Influence diagrams are probabilistic graphical models used to represent and solve decision problems under uncertainty. Sharp numerical values are required to quantify probabilities and utilities. Yet, real models are based on data streams provided by partially reliable sensors or experts. We propose an interval-valued quantification of these parameters to gain realism in the modelling and to an...

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Interval Influence Diagrams

We describe a mechanism for performing probabilistic reasoning in influence diagrams us­ ing interval rather than point valued probabilities. We derive the procedures for node removal (corresponding to conditional expectation) and arc reversal (corresponding to Bayesian condi­ tioning) in influence diagrams where lower bounds on probabilities are stored at each node. The resulting bounds for th...

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Evaluating Influence Diagrams

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Evaluating Influence Diagrams

In this paper we will survey the history of Influence Diagrams from their origin in Decision Theory to modern AI uses. We will compare the various methods that have been used to find an optimal strategy for a given influence diagram. These methods have various advantages under different assumptions and there is a progression to the present of richer, more general solutions. We also look at an a...

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ژورنال

عنوان ژورنال: International Journal of Approximate Reasoning

سال: 2017

ISSN: 0888-613X

DOI: 10.1016/j.ijar.2016.05.004