Reasoning with Contextual Knowledge and Influence Diagrams

Erman Acar, Rafael Peñaloza

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


Influence diagrams (IDs) are well-known formalisms, which extend Bayesian networks to model decision situations under uncertainty. Although they are convenient as a decision theoretic tool, their knowledge representation ability is limited in capturing other crucial notions such as logical consistency. In this article, we complement IDs with the light-weight description logic (DL) EL to overcome such limitations. We consider a setup where DL axioms hold in some contexts, yet the actual context is uncertain. The framework benefits from the convenience of using DL as a domain knowledge representation language and the modelling strength of IDs to deal with decisions over contexts in the presence of contextual uncertainty. We define related reasoning problems and study their computational complexity.
Original languageEnglish
Title of host publicationKR2020
Subtitle of host publicationProceedings of the 17th Conference on Principles of Knowledge Representation and Reasoning. Rhodes, Greece. September 12-18, 2020
EditorsDiego Calvanese, Esra Erdem, Michael Thielscher
PublisherIJCAI Organization
Number of pages10
ISBN (Electronic)780999241172
Publication statusPublished - 20 Aug 2020

Publication series

NameKR Proceedings : Proceedings of the xxth Conference on Principles of Knowledge Representation and Reasoning
PublisherIJCAI Organization
ISSN (Electronic)2334-1033


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