Reasoning with Contextual Knowledge and Influence Diagrams

TL;DR

This paper enhances influence diagrams with description logic EL to better represent domain knowledge and handle contextual uncertainty in decision-making, analyzing the computational complexity of reasoning within this framework.

Contribution

It introduces a novel integration of influence diagrams with description logic EL to improve knowledge representation under contextual uncertainty.

Findings

Framework supports reasoning with uncertain contexts

Analyzes computational complexity of the combined model

Enhances decision-making under uncertainty

Abstract

Influence diagrams (IDs) are well-known formalisms extending 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. 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.