Symbolic LTLf synthesis

  • Shufang Zhu
  • , Lucas M. Tabajara
  • , Jianwen Li*
  • , Geguang Puy
  • , Moshe Y. Vardi
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

97 Scopus citations

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.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1362-1369
Number of pages8
ISBN (Electronic)9780999241103
DOIs
StatePublished - 2017
Externally publishedYes
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume0
ISSN (Print)1045-0823

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Country/TerritoryAustralia
CityMelbourne
Period19/08/1725/08/17

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