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Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification [PDF]

open access: yesIEEE International Conference on Robotics and Automation, 2023
To make robots accessible to a broad audience, it is critical to endow them with the ability to take universal modes of communication, like commands given in natural language, and extract a concrete desired task specification, defined using a formal ...
Jiayi Pan, Glen Chou, D. Berenson
semanticscholar   +1 more source

Gödel-Dummett linear temporal logic [PDF]

open access: yesArtificial Intelligence, 2023
We investigate a version of linear temporal logic whose propositional fragment is G\"odel-Dummett logic (which is well known both as a superintuitionistic logic and a t-norm fuzzy logic).
J. Ozuna   +3 more
semanticscholar   +1 more source

Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks [PDF]

open access: yesIEEE Transactions on robotics, 2022
This article presents a neurosymbolic framework to solve motion planning problems for mobile robots involving temporal goals. The temporal goals are described using temporal logic formulas, such as bounded linear temporal logic (LTL) and co-safe LTL to ...
Xiaowu Sun, Yasser Shoukry
semanticscholar   +1 more source

Complexity of Safety and coSafety Fragments of Linear Temporal Logic [PDF]

open access: yesAAAI Conference on Artificial Intelligence, 2022
Linear Temporal Logic (LTL) is the de-facto standard temporal logic for system specification, whose foundational properties have been studied for over five decades.
A. Artale   +4 more
semanticscholar   +1 more source

Dynamic Linear Time Temporal Logic

open access: hybridBRICS Report Series, 1997
A simple extension of the propositional temporal logic of linear<br />time is proposed. The extension consists of strengthening the until<br />operator by indexing it with the regular programs of propositional<br />dynamic logic (PDL).
Jesper G. Henriksen, P. S. Thiagarajan
openalex   +4 more sources

NL2LTL - a Python Package for Converting Natural Language (NL) Instructions to Linear Temporal Logic (LTL) Formulas

open access: yesAAAI Conference on Artificial Intelligence, 2023
This is a demonstration of our newly released Python package NL2LTL which leverages the latest in natural language understanding (NLU) and large language models (LLMs) to translate natural language instructions to linear temporal logic (LTL) formulas ...
Francesco Fuggitti   +1 more
semanticscholar   +1 more source

Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic

open access: yesInternational Conference on Automated Planning and Scheduling, 2023
We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL). PPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better ...
Luigi Bonassi   +5 more
semanticscholar   +1 more source

A Parallel Linear Temporal Logic Tableau [PDF]

open access: yesElectronic Proceedings in Theoretical Computer Science, 2017
For many applications, we are unable to take full advantage of the potential massive parallelisation offered by supercomputers or cloud computing because it is too hard to work out how to divide up the computation task between processors in such a way ...
John C. McCabe-Dansted, Mark Reynolds
doaj   +1 more source

Intuitionistic Linear Temporal Logics [PDF]

open access: yesACM Transactions on Computational Logic, 2019
We consider intuitionistic variants of linear temporal logic with “next,” “until,” and “release” based on expanding posets : partial orders equipped with an order-preserving transition function. This class of structures gives rise to a logic that we denote ITL e , and by imposing additional ...
Philippe Balbiani   +3 more
openaire   +4 more sources

Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic

open access: yesSystems, 2023
In the context of reinforcement learning (RL), ensuring both safety and performance is crucial, especially in real-world scenarios where mistakes can lead to severe consequences.
Ryeonggu Kwon, Gihwon Kwon
doaj   +1 more source

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