Sequential decision making with partially ordered preferences

Daniel Kikuti, Fabio Gagliardi Cozman, Ricardo Shirota Filho

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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 criteria for consequentialist strategy selection: Γ-Maximin, Γ-Maximax, Γ-Maximix, Interval Dominance, Maximality and E-admissibility. We focus on the popular decision tree and influence diagram representations. Algorithms resort to linear/multilinear programming; we describe implementation and experiments. © 2010 Elsevier B.V. All rights reserved.
Original languageAmerican English
Pages (from-to)1346-1365
Number of pages20
JournalArtificial Intelligence
DOIs
StatePublished - 1 May 2011
Externally publishedYes

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