Contrastive explanations of plans through model restrictions

Benjamin Krarup, Senka Krivic, Daniele Magazzeni, Derek Long, Michael Cashmore, David Smith

Research output: Contribution to journalArticlepeer-review

Abstract

In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user's expectation. We frame Explainable AI Planning in the context of the plan negotiation problem, in which a succession of hypothetical planning problems are generated and solved. The object of the negotiation is for the user to understand and ultimately arrive at a satisfactory plan. We present the results of a user
study that demonstrates that when users ask questions about plans, those questions are contrastive, i.e. "why A rather than B?". We use the data from this study to construct a taxonomy of user questions that often arise during plan negotiation. We formally define our approach to plan negotiation through model restriction as an iterative process. This approach generates hypothetical problems and contrastive plans by restricting the model through constraints implied by user questions. We formally define model-based compilations
in PDDL2.1 of each constraint derived from a user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity. The compilations were implemented as part of an explanation framework that employs iterative model restriction. We demonstrate its benefits in a second user study.
Original languageEnglish
JournalJournal of Artificial Intelligence Research
Publication statusAccepted/In press - 1 Jul 2021

Keywords

  • automated planning
  • artificial intelligence
  • taxonomy

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