On Model Reconciliation: How to Reconcile When Robot Does not Know Human's Model?

TL;DR

This paper introduces a dialog-based method for explaining model differences in AI planning when the robot does not know the human's model, enabling more realistic and flexible explanations through iterative communication.

Contribution

It proposes a novel dialog framework and algorithms for model reconciliation without assuming the robot's knowledge of the human model, implemented using answer set programming.

Findings

Developed algorithms for robot proposals and human responses.

Implemented a system combining imperative programming with answer set programming.

Demonstrated the approach's effectiveness in model reconciliation scenarios.

Abstract

The Model Reconciliation Problem (MRP) was introduced to address issues in explainable AI planning. A solution to a MRP is an explanation for the differences between the models of the human and the planning agent (robot). Most approaches to solving MRPs assume that the robot, who needs to provide explanations, knows the human model. This assumption is not always realistic in several situations (e.g., the human might decide to update her model and the robot is unaware of the updates). In this paper, we propose a dialog-based approach for computing explanations of MRPs under the assumptions that (i) the robot does not know the human model; (ii) the human and the robot share the set of predicates of the planning domain and their exchanges are about action descriptions and fluents' values; (iii) communication between the parties is perfect; and (iv) the parties are truthful. A solution of a MRP is computed through a dialog, defined as a sequence of rounds of exchanges, between the robot and the human. In each round, the robot sends a potential explanation, called proposal, to the human who replies with her evaluation of the proposal, called response. We develop algorithms for computing proposals by the robot and responses by the human and implement these algorithms in a system that combines imperative means with answer set programming using the multi-shot feature of clingo.