@inproceedings{ad8d9ee4dc8843d7b4c692e36c220aea,
title = "Symbolic LTLf synthesis",
abstract = "LTLf synthesis is the process of finding a strategy that satisfies a linear temporal specification over finite traces. An existing solution to this problem relies on a reduction to a DFA game. In this paper, we propose a symbolic framework for LTLf synthesis based on this technique, by performing the computation over a representation of the DFA as a boolean formula rather than as an explicit graph. This approach enables strategy generation by utilizing the mechanism of boolean synthesis. We implement this symbolic synthesis method in a tool called Syft, and demonstrate by experiments on scalable benchmarks that the symbolic approach scales better than the explicit one.",
author = "Shufang Zhu and Tabajara, \{Lucas M.\} and Jianwen Li and Geguang Puy and Vardi, \{Moshe Y.\}",
year = "2017",
doi = "10.24963/ijcai.2017/189",
language = "英语",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "1362--1369",
editor = "Carles Sierra",
booktitle = "26th International Joint Conference on Artificial Intelligence, IJCAI 2017",
address = "美国",
note = "26th International Joint Conference on Artificial Intelligence, IJCAI 2017 ; Conference date: 19-08-2017 Through 25-08-2017",
}