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

• URL: http://arxiv.org/abs/2603.14824v2
• Date: Tue, 24 Mar 2026 02:15:22 GMT
• Title: Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version
• Abstract summary: We study the adoption and properties of GR-deriveds for seeking solutions to classical planning problems.<n>We propose a new divergence-based framework for assessing goal intention, which informs a new class of efficiently-computables.
• Score: 22.281341548124328
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• TodoEvolve: Learning to Architect Agent Planning Systems [68.48983335970901]
  TodoEvolve is a meta-planning paradigm that autonomously synthesizes and dynamically revises task-specific planning.<n>PlanFactory provides a common interface for heterogeneous planning patterns.<n>TodoEvolve consistently surpasses carefully engineered planning modules while maintaining economical API costs and runtime overhead.
  arXiv  Detail & Related papers  (2026-02-08T06:37:01Z)
• Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling [74.41886258801209]
  We propose a two-stage trajectory planning framework that decouples principle alignment from behavior learning.<n>Plan-R1 significantly improves planning safety and feasibility, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-05-23T09:22:19Z)
• Introduction to AI Planning [0.0]
  The notes begin by introducing the state model and move on to exploring classical planning.<n>The most extensive section is dedicated to Hierarchical Task Network (HTN) planning.<n>The lecture notes end with a bonus chapter on the Planning Domain Definition (PDDL) Language.
  arXiv  Detail & Related papers  (2024-12-16T10:38:04Z)
• LHPF: Look back the History and Plan for the Future in Autonomous Driving [10.855426442780516]
  This paper introduces LHPF, an imitation learning planner that integrates historical planning information. Our approach employs a historical intention aggregation module that pools historical planning intentions. Experiments using both real-world and synthetic data demonstrate that LHPF not only surpasses existing advanced learning-based planners in planning performance but also marks the first instance of a purely learning-based planner outperforming the expert.
  arXiv  Detail & Related papers  (2024-11-26T09:30:26Z)
• Parallel Strategies for Best-First Generalized Planning [51.713634067802104]
  Generalized planning (GP) is a research area of AI that studies the automated synthesis of algorithmic-like solutions capable of solving multiple classical planning instances. One of the current advancements has been the introduction of Best-First Generalized Planning (BFGP), a GP algorithm based on a novel solution space that can be explored with search. This paper evaluates the application of parallel search techniques to BFGP, another critical component in closing the performance gap.
  arXiv  Detail & Related papers  (2024-07-31T09:50:22Z)
• LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning [65.86754998249224]
  We develop a novel hybrid planner that leverages a conventional rule-based planner in conjunction with an LLM-based planner. Our approach navigates complex scenarios which existing planners struggle with, produces well-reasoned outputs while also remaining grounded through working alongside the rule-based approach.
  arXiv  Detail & Related papers  (2023-12-30T02:53:45Z)
• A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency [6.87593454486392]
  We consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain.
  arXiv  Detail & Related papers  (2023-07-25T14:51:07Z)
• Imitating Graph-Based Planning with Goal-Conditioned Policies [72.61631088613048]
  We present a self-imitation scheme which distills a subgoal-conditioned policy into the target-goal-conditioned policy. We empirically show that our method can significantly boost the sample-efficiency of the existing goal-conditioned RL methods.
  arXiv  Detail & Related papers  (2023-03-20T14:51:10Z)
• Forethought and Hindsight in Credit Assignment [62.05690959741223]
  We work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated.
  arXiv  Detail & Related papers  (2020-10-26T16:00:47Z)
• Robust Hierarchical Planning with Policy Delegation [6.1678491628787455]
  We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains.
  arXiv  Detail & Related papers  (2020-10-25T04:36:20Z)
• Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors [124.30562402952319]
  The ability to predict and plan into the future is fundamental for agents acting in the world. Current learning approaches for visual prediction and planning fail on long-horizon tasks. We propose a framework for visual prediction and planning that is able to overcome both of these limitations.
  arXiv  Detail & Related papers  (2020-06-23T17:58:56Z)
• Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning [78.65083326918351]
  We consider alternatives to an implicit sequential planning assumption. We propose Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS) for approximating the optimal plan. We show that this algorithmic flexibility over planning order leads to improved results in navigation tasks in grid-worlds.
  arXiv  Detail & Related papers  (2020-04-23T18:08:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.