Related papers: Real-World Planning with PDDL+ and Beyond

Real-World Planning with PDDL+ and Beyond

URL: http://arxiv.org/abs/2402.11901v1
Date: Mon, 19 Feb 2024 07:35:49 GMT
Title: Real-World Planning with PDDL+ and Beyond
Authors: Wiktor Piotrowski, Alexandre Perez
Abstract summary: We present Nyx, a novel PDDL+ planner built to emphasize lightness, simplicity, and, most importantly, adaptability. Nyx can be tailored to virtually any potential real-world application requiring some form of AI Planning, paving the way for wider adoption of planning methods for solving real-world problems.
Score: 55.73913765642435
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Real-world applications of AI Planning often require a highly expressive modeling language to accurately capture important intricacies of target systems. Hybrid systems are ubiquitous in the real-world, and PDDL+ is the standardized modeling language for capturing such systems as planning domains. PDDL+ enables accurate encoding of mixed discrete-continuous system dynamics, exogenous activity, and many other interesting features exhibited in realistic scenarios. However, the uptake in usage of PDDL+ has been slow and apprehensive, largely due to a general shortage of PDDL+ planning software, and rigid limitations of the few existing planners. To overcome this chasm, we present Nyx, a novel PDDL+ planner built to emphasize lightness, simplicity, and, most importantly, adaptability. The planner is designed to be effortlessly customizable to expand its capabilities well beyond the scope of PDDL+. As a result, Nyx can be tailored to virtually any potential real-world application requiring some form of AI Planning, paving the way for wider adoption of planning methods for solving real-world problems.

Related papers

PDDLEGO: Iterative Planning in Textual Environments [56.12148805913657]
Planning in textual environments has been shown to be a long-standing challenge even for current models. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation.
arXiv Detail & Related papers (2024-05-30T08:01:20Z)
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)
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners [85.03486419424647]
KnowNo is a framework for measuring and aligning the uncertainty of large language models. KnowNo builds on the theory of conformal prediction to provide statistical guarantees on task completion.
arXiv Detail & Related papers (2023-07-04T21:25:12Z)
HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN Planning [64.07762708909846]
Real world applications need modelling rich and diverse automated planning problems. hierarchical task network (HTN) formalism does not allow to represent planning problems with numerical and temporal constraints. We propose to fill the gap between HDDL and these operational needs and to extend HDDL by taking inspiration from PDDL 2.1.
arXiv Detail & Related papers (2023-06-12T18:21:23Z)
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)
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning [39.29964085305846]
Methods that use pre-trained large language models directly as planners are currently impractical due to limited correctness of plans. In this work, we introduce a novel alternative paradigm that constructs an explicit world (domain) model in planning domain definition language (PDDL) and then uses it to plan with sound domain-independent planners.
arXiv Detail & Related papers (2023-05-24T08:59:15Z)
HDDL 2.1: Towards Defining an HTN Formalism with Time [0.0]
Real world applications of planning, like in industry and robotics, require modelling rich and diverse scenarios. Their resolution usually requires coordinated and concurrent action executions. In several cases, such planning problems are naturally decomposed in a hierarchical way and expressed by a Hierarchical Task Network formalism. This paper opens discussions on the semantics and the syntax needed to extend HDDL, and illustrate these needs with the modelling of an Earth Observing Satellite planning problem.
arXiv Detail & Related papers (2022-06-03T21:22:19Z)
A Logical Semantics for PDDL+ [4.111899441919164]
PDDL+ is an extension of PDDL2.1 which incorporates fully-featured autonomous processes. Unlike PDDL2.1, PDDL+ lacks a logical semantics, relying instead on state-transitional semantics enriched with hybrid automata semantics for the continuous states. We propose a natural extension of Reiter's situation calculus theories inspired by hybrid automata.
arXiv Detail & Related papers (2021-11-23T00:32:15Z)

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