Satisfiability in Strategy Logic Can Be Easier than Model Checking
Abstract
In the design of complex systems, model-checking and satisfiability arise as two prominent decision problems. While model-checking requires the designed system to be provided in advance, satisfiability allows to check if such a system even exists. With very few exceptions, the second problem turns out to be harder than the first one from a complexity-theoretic standpoint. In this paper, we investigate the connection between the two problems for a non-trivial fragment of Strategy Logic (SL, for short). SL extends LTL with first-order quantifications over strategies, thus allowing to explicitly reason about the strategic abilities of agents in a multi-agent system. Satisfiability for the full logic is known to be highly undecidable, while model-checking is non-elementary.The SL fragment we consider is obtained by preventing strategic quantifications within the scope of temporal operators. The resulting logic is quite powerful, still allowing to express important game-theoretic properties of multi-agent systems, such as existence of Nash and immune equilibria, as well as to formalize the rational synthesis problem. We show that satisfiability for such a fragment is PSPACE-COMPLETE, while its model-checking complexity is 2EXPTIME-HARD. The result is obtained by means of an elegant encoding of the problem into the satisfiability of conjunctive-binding first-order logic, a recently discovered decidable fragment of first-order logic.
Cite
Text
Acar et al. "Satisfiability in Strategy Logic Can Be Easier than Model Checking." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33012638Markdown
[Acar et al. "Satisfiability in Strategy Logic Can Be Easier than Model Checking." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/acar2019aaai-satisfiability/) doi:10.1609/AAAI.V33I01.33012638BibTeX
@inproceedings{acar2019aaai-satisfiability,
title = {{Satisfiability in Strategy Logic Can Be Easier than Model Checking}},
author = {Acar, Erman and Benerecetti, Massimo and Mogavero, Fabio},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {2638-2645},
doi = {10.1609/AAAI.V33I01.33012638},
url = {https://mlanthology.org/aaai/2019/acar2019aaai-satisfiability/}
}