Preferred Explanations: Theory and Generation via Planning

Authors

  • Shirin Sohrabi University of Toronto
  • Jorge Baier Pontificia Universidad Católica de Chile
  • Sheila McIlraith University of Toronto

Abstract

In this paper we examine the general problem of generating preferred explanations for observed behavior with respect to a model of the behavior of a dynamical system. This problem arises in a diversity of applications including diagnosis of dynamical systems and activity recognition. We provide a logical characterization of the notion of an explanation. To generate explanations we identify and exploit a correspondence between explanation generation and planning. The determination of good explanations requires additional domain-specific knowledge which we represent as preferences over explanations. The nature of explanations requires us to formulate preferences in a somewhat retrodictive fashion by utilizing Past Linear Temporal Logic. We propose methods for exploiting these somewhat unique preferences effectively within state-of-the-art planners and illustrate the feasibility of generating (preferred) explanations via planning.

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Published

2011-08-04

How to Cite

Sohrabi, S., Baier, J., & McIlraith, S. (2011). Preferred Explanations: Theory and Generation via Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 261-267. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7845

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning