LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work
Abstract
Synthesis techniques for temporal logic specifications are typically based on exploiting symbolic techniques, as done in model checking. These symbolic techniques typically use backward fixpoint computation. Planning, which can be seen as a specific form of synthesis, is a witness of the success of forward search approaches. In this paper, we develop a forward-search approach to full-fledged Linear Temporal Logic on finite traces (LTLf) synthesis. We show how to compute the Deterministic Finite Automaton (DFA) of an LTLf formula on-the-fly, while performing an adversarial forward search towards the final states, by considering the DFA as a sort of AND-OR graph. Our approach is characterized by branching on suitable propositional formulas, instead of individual evaluations, hence radically reducing the branching factor of the search space. Specifically, we take advantage of techniques developed for knowledge compilation, such as Sentential Decision Diagrams (SDDs), to implement the approach efficiently.
Cite
Text
De Giacomo et al. "LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/359Markdown
[De Giacomo et al. "LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/giacomo2022ijcai-ltlf/) doi:10.24963/IJCAI.2022/359BibTeX
@inproceedings{giacomo2022ijcai-ltlf,
title = {{LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work}},
author = {De Giacomo, Giuseppe and Favorito, Marco and Li, Jianwen and Vardi, Moshe Y. and Xiao, Shengping and Zhu, Shufang},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {2591-2598},
doi = {10.24963/IJCAI.2022/359},
url = {https://mlanthology.org/ijcai/2022/giacomo2022ijcai-ltlf/}
}