Towards Decision Making via Expressive Probabilistic Ontologies

Abstract

Abstract. We propose a framework for automated multi-attribute decision making, employing the probabilistic non-monotonic description logics proposed by Lukasiewicz in 2008. Using this framework, we can model artificial agents in decision-making situation, wherein background knowledge, 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.

Reasoning with Contextual Knowledge and Influence Diagrams

“…In addition to the probabilistic logics mentioned in the introduction, some earlier works (Acar, Thorne, and Stuckenschmidt 2015;Acar et al 2017) employed (probabilistic) DLs in a decision-theoretic setting. However, neither addressed observations, nor contextual reasoning.…”