Content: Publications

Complexity of Projection with Stochastic Actions in a Probabilistic Description Logic

In Proc. Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2018), pp. 514--523

Authors:Benjamin Zarrieß
Type:Article in Conference Proceedings
Publication Date:October 2018
Download:Zarriess2018a.pdf
Conference:Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2018)
Editor:Michael Thielscher, Francesca Toni, Frank Wolter

Abstract: We consider an action language extended with quantitative notions of uncertainty. In our setting, the initial beliefs of an agent are represented as a probabilistic knowledge base with axioms formulated in the Description Logic ALCO. Action descriptions describe the possibly context-sensitive and non-deterministic effects of actions and provide likelihood distributions over the different possible outcomes of actions. In this paper, we prove decidability of the projection problem which is the basic reasoning task needed for predicting the outcome of action sequences. Furthermore, we investi gate how the non-determinism in the action model affects the complexity of the projection problem.

BibTeX
@InProceedings{hybris-a1-probabilistic-dl-projection-2018,
  title =	{{Complexity of Projection with Stochastic Actions in a Probabilistic Description Logic}},
  author =	{Benjamin Zarrieß},
  booktitle =	{Proc. of Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2018)},
  year =	{2018},
  editor =	{Michael Thielscher, Francesca Toni, Frank Wolter},
  publisher =	{AAAI Press},
  pages =	{514--523},
}