Satisfiability Checking of Strategy Logic with Simple Goals

In this paper, we introduce a new method of the satisfiability (SAT) checking for Simple-Goal Strategy Logic (SL[SG]), using symbolic Boolean model encoding and the SAT Modulo Monotonic Theories techniques, which was implemented into the tool SGSAT. To the best of our knowledge, this is the only tool solving the SAT problem for SL[SG]. Its applications include process synthesis, developing controllers as well as automatic planners in multi-agent scenarios.

Quasipolynomial Computation of Nested Fixpoints

It is well-known that the winning region of a parity game with n nodes and k priorities can be computed as a k-nested fixpoint of a suitable function; straightforward computation of this nested fixpoint requires $$\mathcal {O}(n^{\frac{k}{2}})$$ O ( n k 2 ) iterations of the function. Calude et al.’s recent quasipolynomial-time parity game solving algorithm essentially shows how to compute the same fixpoint in only quasipolynomially many iterations by reducing parity games to quasipolynomially sized safety games. Universal graphs have been used to modularize this transformation of parity games to equivalent safety games that are obtained by combining the original game with a universal graph. We show that this approach naturally generalizes to the computation of solutions of systems of any fixpoint equations over finite lattices; hence, the solution of fixpoint equation systems can be computed by quasipolynomially many iterations of the equations. We present applications to modal fixpoint logics and games beyond relational semantics. For instance, the model checking problems for the energy $$\mu $$ μ -calculus, finite latticed $$\mu $$ μ -calculi, and the graded and the (two-valued) probabilistic $$\mu $$ μ -calculus – with numbers coded in binary – can be solved via nested fixpoints of functions that differ substantially from the function for parity games but still can be computed in quasipolynomial time; our result hence implies that model checking for these $$\mu $$ μ -calculi is in $$\textsc {QP}$$ QP . Moreover, we improve the exponent in known exponential bounds on satisfiability checking.

SAT-Based ATL Satisfiability Checking

Synthesis of models and strategies is a very important task in software engineering. The main problem here consists in checking the satisfiability of formulae expressing the specification of a system to be implemented. This paper puts forward a novel method for deciding the satisfiability of formulae of Alternating-time Temporal Logic (ATL) under perfect and imperfect information. The synthesised models of strategic games are often minimal. The method expands the one for CTL exploiting SAT Modulo Monotonic Theories (SMMT) solvers. Our tool MsATL combines SMMT solvers with two existing ATL model checkers: MCMAS and STV. This is the first ever tool for checking the satisfiability of imperfect information ATL. The experimental results show that, similarly to the CTL case, our approach appears to be very efficient and can quickly check the satisfiability of large ATL formulae that have been out of reach of the existing approaches.

SAT-Based Explicit LTLf Satisfiability Checking

We present a SAT-based framework for LTLf (Linear Temporal Logic on Finite Traces) satisfiability checking. We use propositional SAT-solving techniques to construct a transition system for the input LTLf formula; satisfiability checking is then reduced to a path-search problem over this transition system. Furthermore, we introduce CDLSC (Conflict-Driven LTLf Satisfiability Checking), a novel algorithm that leverages information produced by propositional SAT solvers from both satisfiability and unsatisfiability results. Experimental evaluations show that CDLSC outperforms all other existing approaches for LTLf satisfiability checking, by demonstrating an approximate four-fold speed-up compared to the second-best solver.

Probabilistic Alternating-Time µ-Calculus

Reasoning about strategic abilities is key to an AI system consisting of multiple agents with random behaviors. We propose a probabilistic extension of Alternating µ-Calculus (AMC), named PAMC, for reasoning about strategic abilities of agents in stochastic multi-agent systems. PAMC subsumes existing logics AMC and PµTL. The usefulness of PAMC is exemplified by applications in genetic regulatory networks. We show that, for PAMC, the model checking problem is in UP∩co-UP, and the satisfiability problem is EXPTIME-complete, both of which are the same as those for AMC. Moreover, PAMC admits the small model property. We implement the satisfiability checking procedure in a tool PAMCSolver.