Symbolic LTLf Best-Effort Synthesis
Artificial Intelligence
2023-08-30 v1 Formal Languages and Automata Theory
Computer Science and Game Theory
Logic in Computer Science
Robotics
Abstract
We consider an agent acting to fulfil tasks in a nondeterministic environment. When a strategy that fulfills the task regardless of how the environment acts does not exist, the agent should at least avoid adopting strategies that prevent from fulfilling its task. Best-effort synthesis captures this intuition. In this paper, we devise and compare various symbolic approaches for best-effort synthesis in Linear Temporal Logic on finite traces (LTLf). These approaches are based on the same basic components, however they change in how these components are combined, and this has a significant impact on the performance of the approaches as confirmed by our empirical evaluations.
Keywords
Cite
@article{arxiv.2308.15178,
title = {Symbolic LTLf Best-Effort Synthesis},
author = {Giuseppe De Giacomo and Gianmarco Parretti and Shufang Zhu},
journal= {arXiv preprint arXiv:2308.15178},
year = {2023}
}
Comments
To appear at EUMAS2023