Related papers: Planning with Minimal Disruption

Planning with Minimal Disruption

URL: http://arxiv.org/abs/2508.15358v1
Date: Thu, 21 Aug 2025 08:38:17 GMT
Title: Planning with Minimal Disruption
Authors: Alberto Pozanco, Marianela Morales, Daniel Borrajo, Manuela Veloso,
Abstract summary: In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals.<n>In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption.
Score: 9.722824469961925
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both objectives.

Related papers

Satisficing and Optimal Generalised Planning via Goal Regression (Extended Version) [16.43772461453855]
Generalised planning (GP) refers to the task of synthesising programs that solve families of related planning problems.<n>We introduce a novel, yet simple method for GP: given a set of training problems, for each problem, compute an optimal plan for each goal atom in some order.<n>We formalise and prove the conditions under which our method is guaranteed to learn valid generalised plans and state space pruning axioms for search.
arXiv Detail & Related papers (2025-11-14T09:16:32Z)
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking [109.09735490692202]
We propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning.<n> Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.
arXiv Detail & Related papers (2025-05-05T02:38:58Z)
A Planning Compilation to Reason about Goal Achievement at Planning Time [9.722824469961925]
We propose a compilation that extends the original planning task with commit actions that enforce the persistence of specific goals once achieved.<n> Experimental results indicate that solving the reformulated tasks does not incur on any additional overhead both when performing optimal and suboptimal planning.
arXiv Detail & Related papers (2025-03-12T17:00:37Z)
Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following [62.10809033451526]
This work focuses on building a task planner for Embodied Instruction Following (EIF) using Large Language Models (LLMs)<n>We frame the task as a Partially Observable Markov Decision Process (POMDP) and aim to develop a robust planner under a few-shot assumption.<n>Our experiments on the ALFRED dataset indicate that our planner achieves competitive performance under a few-shot assumption.
arXiv Detail & Related papers (2024-12-27T10:05:45Z)
On Computing Plans with Uniform Action Costs [10.621487250485897]
This paper adapts three uniformity metrics to automated planning, and introduces planning-based compilations that allow to lexicographically optimize sum of action costs and action costs uniformity. Experimental results both in well-known and novel planning benchmarks show that the reformulated tasks can be effectively solved in practice to generate uniform plans.
arXiv Detail & Related papers (2024-02-15T11:00:28Z)
Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints [56.283944756315066]
We propose an alternative TAMP approach that unifies task and motion planning into a single search. Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
arXiv Detail & Related papers (2023-12-29T14:00:20Z)
Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems. We propose a task-agnostic method named 'planning as in-painting' The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z)
AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
arXiv Detail & Related papers (2023-05-26T05:52:27Z)
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)
Multi-tier Automated Planning for Adaptive Behavior (Extended Version) [0.4129225533930965]
We propose a multi-tier framework for planning that allows the specification of different sets of assumptions. We show how to solve problem instances by a succinct compilation to a form of non-deterministic planning.
arXiv Detail & Related papers (2020-02-27T21:16:01Z)

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