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相关论文: HDDL 2.1: Towards Defining a Formalism and a Seman…

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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…

人工智能 · 计算机科学 2022-06-07 D. Pellier , H. Fiorino , M. Grand , A. Albore , R. Bailon-Ruiz

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with…

人工智能 · 计算机科学 2019-11-14 D. Höller , G. Behnke , P. Bercher , S. Biundo , H. Fiorino , D. Pellier , R. Alford

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems as task decompositions, and many techniques have been proposed to solve them. However, few works have been done on temporal HTN. This is…

人工智能 · 计算机科学 2023-06-14 Nicolas Cavrel , Damien Pellier , Humbert Fiorino

In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space…

人工智能 · 计算机科学 2011-06-24 M. Fox , D. Long

The Hierarchical Task Network ({\sf HTN}) formalism is very expressive and used to express a wide variety of planning problems. In contrast to the classical {\sf STRIPS} formalism in which only the action model needs to be specified, the…

人工智能 · 计算机科学 2022-06-15 M. Grand , H. Fiorino , D. Pellier

Hierarchical Task Network (HTN) planning is a popular approach that cuts down on the classical planning search space by relying on a given hierarchical library of domain control knowledge. This provides an intuitive methodology for…

机器人学 · 计算机科学 2014-06-13 Raphaël Lallement , Lavindra de Silva , Rachid Alami

To enable non-experts to specify long-horizon, multi-robot collaborative tasks, language models are increasingly used to translate natural language commands into formal specifications. However, because translation can occur in multiple…

机器人学 · 计算机科学 2024-12-06 Shaojun Xu , Xusheng Luo , Yutong Huang , Letian Leng , Ruixuan Liu , Changliu Liu

Research in robotic planning with temporal logic specifications, such as Linear Temporal Logic (LTL), has relied on single formulas. However, as task complexity increases, LTL formulas become lengthy, making them difficult to interpret and…

机器人学 · 计算机科学 2025-06-06 Xusheng Luo , Changliu Liu

While Large Language Models (LLM) enable non-experts to specify open-world multi-robot tasks, the generated plans often lack kinematic feasibility and are not efficient, especially in long-horizon scenarios. Formal methods like Linear…

机器人学 · 计算机科学 2026-02-11 Shuyuan Hu , Tao Lin , Kai Ye , Yang Yang , Tianwei Zhang

PDDL2.1 was designed to push the envelope of what planning algorithms can do, and it has succeeded. It adds two important features: durative actions,which take time (and may have continuous effects); and objective functions for measuring…

人工智能 · 计算机科学 2011-09-27 D. McDermott

Solving complex planning problems requires Large Language Models (LLMs) to explicitly model the state transition to avoid rule violations, comply with constraints, and ensure optimality-a task hindered by the inherent ambiguity of natural…

人工智能 · 计算机科学 2025-05-09 Zhouliang Yu , Yuhuan Yuan , Tim Z. Xiao , Fuxiang Frank Xia , Jie Fu , Ge Zhang , Ge Lin , Weiyang Liu

Large Language Models have been found to create plans that are neither executable nor verifiable in grounded environments. An emerging line of work demonstrates success in using the LLM as a formalizer to generate a formal representation of…

计算与语言 · 计算机科学 2025-06-03 Cassie Huang , Li Zhang

Hierarchical Task Network (HTN) planning is a practical and efficient approach to planning when the 'standard operating procedures' for a domain are available. Like Belief-Desire-Intention (BDI) agent reasoning, HTN planning performs…

人工智能 · 计算机科学 2021-07-06 Lavindra de Silva

HTN planning is a variation of classical planning where, instead of searching for a linear sequence of actions, an algorithm decomposes higher-level tasks using a method library until only executable actions remain. On one hand, this allows…

PDDL was originally conceived and constructed as a lingua franca for the International Planning Competition. PDDL2.1 embodies a set of extensions intended to support the expression of something closer to real planning problems. This…

人工智能 · 计算机科学 2011-10-13 M. S. Boddy

Cloud providers are facing a complex problem in configuring software applications ready for deployment on their infrastructures. Hierarchical Task Network (HTN) planning can provide effective means to solve such deployment problems. We…

分布式、并行与集群计算 · 计算机科学 2021-05-31 Ilche Georgievski

Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. We…

人工智能 · 计算机科学 2026-03-13 Andrea Micheli , Enrico Scala , Alessandro Valentini

Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. Although there are a few approaches proposed to target at the challenging problem, they generally cannot scale to…

人工智能 · 计算机科学 2022-12-13 Kebing Jin , Yingkai Xiao , Hankz Hankui Zhuo , Renyong Ma

Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on a single formula for individual or groups of robots. But with increasing task complexity, LTL formulas…

机器人学 · 计算机科学 2024-05-27 Xusheng Luo , Shaojun Xu , Ruixuan Liu , Changliu Liu

Hierarchies are the most common structure used to understand the world better. In galaxies, for instance, multiple-star systems are organised in a hierarchical system. Then, governmental and company organisations are structured using a…

人工智能 · 计算机科学 2014-03-31 Ilche Georgievski , Marco Aiello
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