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.
Acar, E., Thorne, C., & Stuckenschmidt, H. (2015). Towards decision making via expressive probabilistic ontologies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9346, pp. 52–68). Springer Verlag. https://doi.org/10.1007/978-3-319-23114-3_4