Towards decision making via expressive probabilistic ontologies

Erman Acar*, Camilo Thorne, Heiner Stuckenschmidt

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


We propose a framework for automated multi-attribute deci- sion making, employing the probabilistic non-monotonic description log- ics proposed by Lukasiewicz in 2008. Using this framework, we can model artificial agents in decision-making situation, wherein background knowl- edge, available alternatives and weighted attributes are represented via probabilistic ontologies. It turns out that extending traditional utility theory with such description logics, enables us to model decision-making problems where probabilistic ignorance and default reasoning plays an important role. We provide several decision functions using the notions of expected utility and probability intervals, and study their properties.

Original languageEnglish
Title of host publicationAlgorithmic Decision Theory
Subtitle of host publication4th International Conference, ADT 2015, Lexington, KY, USA, September 27-30, 2015, Proceedings
EditorsToby Walsh
PublisherSpringer Verlag
Number of pages17
ISBN (Electronic)9783319231143
ISBN (Print)9783319231136
Publication statusPublished - 2015
Externally publishedYes
Event4th International Conference on Algorithmic Decision Theory, ADT 2015 - Lexington, United States
Duration: 27 Sept 201530 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Algorithmic Decision Theory, ADT 2015
Country/TerritoryUnited States


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