Handling model uncertainty and multiplicity in explanations via model reconciliation

Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati

Research output: Contribution to journalConference article

13 Scopus citations

Abstract

Model reconciliation has been proposed as a way for an agent to explain its decisions to a human who may have a different understanding of the same planning problem by explaining its decisions in terms of these model differences. However, often the human’s mental model (and hence the difference) is not known precisely and such explanations cannot be readily computed. In this paper, we show how the explanation generation process evolves in the presence of such model uncertainty or incompleteness by generating conformant explanations that are applicable to a set of possible models. We also show how such explanations can contain superfluous information and how such redundancies can be reduced using conditional explanations to iterate with the human to attain common ground. Finally, we will introduce an anytime version of this approach and empirically demonstrate the trade-offs involved in the different forms of explanations in terms of the computational overhead for the agent and the communication overhead for the human. We illustrate these concepts in three well-known planning domains as well as in a demonstration on a robot involved in a typical search and reconnaissance scenario with an external human supervisor.

Original languageEnglish (US)
Pages (from-to)518-526
Number of pages9
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
Volume2018-June
StatePublished - Jan 1 2018
Event28th International Conference on Automated Planning and Scheduling, ICAPS 2018 - Delft, Netherlands
Duration: Jun 24 2018Jun 29 2018

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management

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