Synthesizing Good-Enough Strategies for LTLf Specifications
Yong Li, Andrea Turrini, Moshe Y. Vardi, Lijun Zhang
Abstract
We consider the problem of synthesizing good-enough (GE)-strategies for linear temporal logic (LTL) over finite traces or LTLf for short. The problem of synthesizing GE-strategies for an LTL formula φ over infinite traces reduces to the problem of synthesizing winning strategies for the formula (∃Oφ)⇒φ where O is the set of propositions controlled by the system. We first prove that this reduction does not work for LTLf formulas. Then we show how to synthesize GE-strategies for LTLf formulas via the Good-Enough (GE)-synthesis of LTL formulas. Unfortunately, this requires to construct deterministic parity automata on infinite words, which is computationally expensive. We then show how to synthesize GE-strategies for LTLf formulas by a reduction to solving games played on deterministic Büchi automata, based on an easier construction of deterministic automata on finite words. We show empirically that our specialized synthesis algorithm for GE-strategies outperforms the algorithms going through GE-synthesis of LTL formulas by orders of magnitude.
BibTeX
@inproceedings{ijcai2021p570,
title = {Synthesizing Good-Enough Strategies for LTLf Specifications},
author = {Li, Yong and Turrini, Andrea and Vardi, Moshe Y. and Zhang, Lijun},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4144--4151},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/570},
url = {https://doi.org/10.24963/ijcai.2021/570},
}