Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning

• URL: http://arxiv.org/abs/2204.09960v2
• Date: Fri, 22 Apr 2022 11:40:14 GMT
• Title: Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning
• Authors: Giuseppe De Giacomo, Marco Favorito, Francesco Fuggitti
• Abstract summary: We study temporally extended goals expressed in Pure-Past (PPLTL) We devise a technique to translate planning for PPLTL goals into standard planning. Our translation enables state-of-the-art tools, such as FD or MyND, to handle PPLTL goals seamlessly.
• Score: 24.40306100502023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study temporally extended goals expressed in Pure-Past LTL (PPLTL). PPLTL is particularly interesting for expressing goals since it allows to express sophisticated tasks as in the Formal Methods literature, while the worst-case computational complexity of Planning in both deterministic and nondeterministic domains (FOND) remains the same as for classical reachability goals. However, while the theory of planning for PPLTL goals is well understood, practical tools have not been specifically investigated. In this paper, we make a significant leap forward in the construction of actual tools to handle PPLTL goals. We devise a technique to polynomially translate planning for PPLTL goals into standard planning. We show the formal correctness of the translation, its complexity, and its practical effectiveness through some comparative experiments. As a result, our translation enables state-of-the-art tools, such as FD or MyND, to handle PPLTL goals seamlessly, maintaining the impressive performances they have for classical reachability goals.

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)
• Directed Exploration in Reinforcement Learning from Linear Temporal Logic [59.707408697394534]
  Linear temporal logic (LTL) is a powerful language for task specification in reinforcement learning. We show that the synthesized reward signal remains fundamentally sparse, making exploration challenging. We show how better exploration can be achieved by further leveraging the specification and casting its corresponding Limit Deterministic B"uchi Automaton (LDBA) as a Markov reward process.
  arXiv  Detail & Related papers  (2024-08-18T14:25:44Z)
• 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)
• 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)
• Learning adaptive planning representations with natural language guidance [90.24449752926866]
  This paper describes Ada, a framework for automatically constructing task-specific planning representations. Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks.
  arXiv  Detail & Related papers  (2023-12-13T23:35:31Z)
• Conformal Temporal Logic Planning using Large Language Models [27.571083913525563]
  We consider missions that require accomplishing multiple high-level sub-tasks expressed in natural language (NL), in a temporal and logical order. Our goal is to design plans, defined as sequences of robot actions, accomplishing-NL tasks. We propose HERACLEs, a hierarchical neuro-symbolic planner that relies on a novel integration of existing symbolic planners.
  arXiv  Detail & Related papers  (2023-09-18T19:05:25Z)
• Designing Behavior Trees from Goal-Oriented LTLf Formulas [3.3674998206524465]
  This paper shows how to turn goals specified using a subset of Linear Temporal Logic (LTL) into a behavior tree (BT) BT guarantees that successful traces satisfy the goal.
  arXiv  Detail & Related papers  (2023-07-12T18:29:37Z)
• Efficient Learning of High Level Plans from Play [57.29562823883257]
  We present Efficient Learning of High-Level Plans from Play (ELF-P), a framework for robotic learning that bridges motion planning and deep RL. We demonstrate that ELF-P has significantly better sample efficiency than relevant baselines over multiple realistic manipulation tasks.
  arXiv  Detail & Related papers  (2023-03-16T20:09:47Z)
• Reinforcement Learning for General LTL Objectives Is Intractable [10.69663517250214]
  We formalize the problem under the probably correct learning in Markov decision processes (PACMDP) framework. Our result implies it is impossible for a reinforcement-learning algorithm to obtain a PAC-MDP guarantee on the performance of its learned policy.
  arXiv  Detail & Related papers  (2021-11-24T18:26:13Z)
• Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models [26.530274055506453]
  Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve. We develop a novel approach that is capable of recognizing temporally extended goals.
  arXiv  Detail & Related papers  (2021-03-22T09:46:03Z)

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.