Title

Probabilistic Lexicographic Preference Trees

Document Type

Conference Proceeding

Publication Date

1-1-2021

Subject Area

ARRAY(0x559695235898)

Abstract

We introduce probabilistic lexicographic preference trees (or PrLPTs for short). We show that they offer intuitive and often compact representations of non-deterministic qualitative preferences over alternatives in multi-attribute (or, combinatorial) binary domains. We specify how a PrLPT defines the probability that a given outcome has a given rank, and the probability that a given outcome is preferred to another one, and show how to compute these probabilities in polynomial time. We also show that computing outcomes that are optimal with the probability equal to or exceeding a given threshold for some classes of PrLP-trees is in P, but for some other classes the problem is NP-hard.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

13023 LNAI

First Page

86

Last Page

100

Digital Object Identifier (DOI)

10.1007/978-3-030-87756-9_6

ISSN

03029743

E-ISSN

16113349

ISBN

9783030877552

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