Satisfiability in Strategy Logic Can Be Easier than Model Checking

Authors: Erman Acar, Massimo Benerecetti, Fabio Mogavero2638-2645

AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical 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.
Researcher Affiliation Academia 1Vrije Universiteit Amsterdam, The Netherlands 2Universit a di Napoli Federico II , Italy
Pseudocode Yes Algorithm 1: FSL[CG] Satisfiability Procedure. signature sat: FSL[CG] B function sat(ϕ)
Open Source Code No The paper is theoretical and focuses on logical complexity and proofs; it does not mention releasing source code.
Open Datasets No The paper is theoretical and does not involve empirical datasets or training data.
Dataset Splits No The paper is theoretical and does not involve empirical data requiring validation splits.
Hardware Specification No The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are provided.
Software Dependencies No The paper is theoretical and does not describe empirical experiments, thus no software dependencies with version numbers are provided.
Experiment Setup No The paper is theoretical and does not describe empirical experiments or specific hyperparameter/training settings.