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

2026-03-16

AI Summary

This work proposes an intention-driven heuristic approach to enhance the efficiency of classical planning. Inspired by intention modeling in goal recognition, the authors introduce—for the first time—a reversal of trajectory-to-goal directedness evaluation into the planning domain, constructing a novel heuristic function to guide search. By integrating probabilistic intention inference with classical planning, they design a computationally efficient heuristic evaluation framework. Two new heuristics derived from this framework have been incorporated into state-of-the-art planners and demonstrate significant performance improvements across multiple benchmark domains, thereby validating the effectiveness of leveraging goal recognition perspectives to empower classical planning.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Heuristic Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 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.

Problem

• goal recognition
• classical planning
• heuristics
• intention models
• planning

Innovation

• goal recognition
• classical planning
• intention-based heuristics
• planning heuristics
• probabilistic intention