Synthesizing Good-Enough Strategies for LTLf Specifications

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.

Cite

Text

Li et al. "Synthesizing Good-Enough Strategies for LTLf Specifications." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/570

Markdown

[Li et al. "Synthesizing Good-Enough Strategies for LTLf Specifications." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/li2021ijcai-synthesizing/) doi:10.24963/IJCAI.2021/570

BibTeX

@inproceedings{li2021ijcai-synthesizing,
  title     = {{Synthesizing Good-Enough Strategies for LTLf Specifications}},
  author    = {Li, Yong and Turrini, Andrea and Vardi, Moshe Y. and Zhang, Lijun},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {4144-4151},
  doi       = {10.24963/IJCAI.2021/570},
  url       = {https://mlanthology.org/ijcai/2021/li2021ijcai-synthesizing/}
}