Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

• URL: http://arxiv.org/abs/2306.05069v1
• Date: Thu, 8 Jun 2023 09:34:38 GMT
• Title: Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs
• Authors: Masood Feyzbakhsh Rankooh and Tomi Janhunen
• Abstract summary: We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be captured with stable models of a logic program. We introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models.
• Score: 4.020523898765405
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We establish a novel relation between delete-free planning, an important task for the AI Planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans, with our diagnostic encoding outperforming state-of-the-art approaches to relaxed planning regardless of the given time limit when measured on a wide collection of STRIPS planning benchmarks.

Related papers

• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)
• Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
  This work lays the foundations for improving planning capabilities of large language models (LLMs) We construct a comprehensive benchmark suite encompassing both classical planning benchmarks and natural language scenarios. We investigate the use of many-shot in-context learning to enhance LLM planning, exploring the relationship between increased context length and improved planning performance.
  arXiv  Detail & Related papers  (2024-06-18T22:57:06Z)
• Temporal Planning via Interval Logic Satisfiability for Autonomous Systems [0.0]
  We consider formulations of temporal planning where intervals are associated with both action and fluent atoms and relations between these are given as sentences in Allen's Interval Logic. We propose a notion of planning graphs that can account for complex relations between actions and fluents as a Constraint Programming (CP) model. We demonstrate our algorithm outperforms existing PDDL 2.1 planners in the case studies.
  arXiv  Detail & Related papers  (2024-06-14T02:21:53Z)
• 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)
• Lifted Sequential Planning with Lazy Constraint Generation Solvers [28.405198103927955]
  This paper studies the possibilities made open by the use of Lazy Clause Generation (LCG) based approaches to Constraint Programming (CP) We propose a novel CP model based on seminal ideas on so-called lifted causal encodings for planning as satisfiability. We report that for planning problem instances requiring fewer plan steps our methods compare very well with the state-of-the-art in optimal sequential planning.
  arXiv  Detail & Related papers  (2023-07-17T04:54:58Z)
• 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)
• A Framework for Neurosymbolic Robot Action Planning using Large Language Models [3.0501524254444767]
  We present a framework aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs) into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL) Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.
  arXiv  Detail & Related papers  (2023-03-01T11:54:22Z)
• Gradient-Based Mixed Planning with Discrete and Continuous Actions [34.885999774739055]
  We propose a quadratic-based framework to simultaneously optimize continuous parameters and actions of candidate plans. The framework is combined with a module to estimate the best plan candidate to transit initial state to the goal based on relaxation.
  arXiv  Detail & Related papers  (2021-10-19T14:21:19Z)
• 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)
• STRIPS Action Discovery [67.73368413278631]
  Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. We propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown.
  arXiv  Detail & Related papers  (2020-01-30T17:08:39Z)

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.