Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version

TL;DR

This paper introduces a novel divergence-based framework for goal recognition heuristics in classical planning, demonstrating that intention-informed heuristics can improve planner performance.

Contribution

It proposes a new framework for deriving heuristics from goal recognition models and shows their effectiveness in enhancing classical planning algorithms.

Findings

Derived two new heuristics based on intention models

Showed heuristics improve planner performance

Provided foundational insights into intention-based heuristics

Abstract

Classical planning aims to find a sequence of actions, a plan, that maps a starting state into one of the goal states. If a trajectory appears to be leading to the goal, should we prioritise exploring it? Seminal work in goal recognition (GR) has defined GR in terms of a classical planning problem, adopting classical solvers and heuristics to recognise plans. We come full circle, and study the adoption and properties of GR-derived heuristics for seeking solutions to classical planning problems. We propose a new divergence-based framework for assessing goal intention, which informs a new class of efficiently-computable heuristics. As a proof of concept, we derive two such heuristics, and show that they can already yield improvements for top-scoring classical planners. Our work provides foundational knowledge for understanding and deriving probabilistic intention-based heuristics for planning.