Argumentation for Evaluative Explanations of PDDL Plans

Anna Collins

Keywords: argumentation, explainable AI, planning

TL;DR: A framework for using causal structure of plans for evaluative explanations.

Abstract: For robotics applications, clear communication between the AI planning system and its human overseer is crucial. Depending on the level of expertise the human has, the availability of live sensor data, and the accuracy of this data, accurate decision-making can be challenging. This paper presents an approach to evaluating a plan's causal structure and a user's decision to remove an action from a plan using domain-specific argumentation schemes. The approach is designed in a modular way and is intended to be used with any PDDL (Planning Domain Definition Language) planning system and easily customizable to its intended application.