Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations



  • Joseph Kim
  • Christian Muise
  • Ankit Shah
  • Shubham Agarwal
  • Julie Shah

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Temporal logics are useful for providing concise descriptions of system behavior, and have been successfully used as a language for goal definitions in task planning. Prior works on inferring temporal logic specifications have focused on “summarizing” the input dataset – i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present BayesLTL – a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic (LTL) specifications. We demonstrate the robustness and scalability of our model for inferring accurate specifications from noisy data and across various benchmark planning domains.

Please cite our work using the BibTeX below.

  title     = {Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations},
  author    = {Kim, Joseph and Muise, Christian and Shah, Ankit and Agarwal, Shubham and Shah, Julie},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {5591--5598},
  year      = {2019},
  month     = {7},
  doi       = {10.24963/ijcai.2019/776},
  url       = {},
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