paper-with-me

홈 › Papers

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

2022-04-21 · Giuseppe De Giacomo, Marco Favorito, Francesco Fuggitti

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.

📄 PDF Abstract BibTeX arXiv:2204.09960

Code (0)

등록된 구현이 없습니다.

Tasks

Translation

Similar Papers 제목 키워드 기반

Temporally Extended Goal Recognition in Fully Observable Non-Deterministic Domain Models

2023-06-14 · Ramon Fraga Pereira, Francesco Fuggitti, Felipe Meneguzzi, Giuseppe De Giacomo

Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of goal hypotheses, a domain model, and a sequence of observations (i.e., a sample of the plan executed in t…

Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models

2021-03-22 · Ramon Fraga Pereira, Francesco Fuggitti, Giuseppe De Giacomo

Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as a sample of the plan being executed in …

TIDE: A Trace-Informed Depth-First Exploration for Planning with Temporally Extended Goals

2026-01-17 · Yuliia Suprun, Khen Elimelech, Lydia E. Kavraki, Moshe Y. Vardi arxiv

Task planning with temporally extended goals (TEGs) is a critical challenge in AI and robotics, enabling agents to achieve complex sequences of objectives over time rather than addressing isolated, immediate tasks. Linea…

Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning

2023-07-20 · Zhifeng Qian, Mingyu You, Hongjun Zhou, Xuanhui Xu 외

Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally e…

Disentanglementreinforcement-learningReinforcement Learning

Probabilistic Planning with Partially Ordered Preferences over Temporal Goals

2022-09-25 · Hazhar Rahmani, Abhishek N. Kulkarni, Jie Fu

In this paper, we study planning in stochastic systems, modeled as Markov decision processes (MDPs), with preferences over temporally extended goals. Prior work on temporal planning with preferences assumes that the user…