Bayesian Inference of Linear Temporal Logic Specifications for Contrastive
Explanations
Abstract
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