Decision Making by Credal Nets

A. Antonucci, C. P. de Campos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. This feature makes the model particularly suited for the implementation of classifiers and knowledge-based systems. When working with sets of (instead of single) probability distributions, the identification of the optimal option can be based on different criteria, some of them eventually leading to multiple choices. Yet, most of the inference algorithms for credal nets are designed to compute only the bounds of the posterior probabilities. This prevents some of the existing criteria from being used. To overcome this limitation, we present two simple transformations for credal nets which make it possible to compute decisions based on the maximality and E-admissibility criteria without any modification in the inference algorithms. We also prove that these decision problems have the same complexity of standard inference, being NP^PP-hard for general credal nets and NP-hard for polytrees.
Original languageEnglish
Title of host publicationInternational Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)
PublisherIEEE Computer Society
Pages201-204
Number of pages4
ISBN (Print)978-1-4577-0676-9
DOIs
Publication statusPublished - 2011

Bibliographical note

(oral presentation, blind peer reviewed)

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