5 research outputs found
Efficient Instantiation of Parameterised Boolean Equation Systems to Parity Games
Parameterised Boolean Equation Systems (PBESs) are sequences of Boolean fixed point equations with data variables, used for, e.g., verification of modal μ-calculus formulae for process algebraic specifications with data. Solving a PBES is usually done by instantiation to a Parity Game and then solving the game. Practical game solvers exist, but the instantiation step is the bottleneck. We enhance the instantiation in two steps. First, we transform the PBES to a Parameterised Parity Game (PPG), a PBES with each equation either conjunctive or disjunctive. Then we use LTSmin, that offers transition caching, efficient storage of states and both distributed and symbolic state space generation, for generating the game graph. To that end we define a language module for LTSmin, consisting of an encoding of variables with parameters into state vectors, a grouped transition relation and a dependency matrix to indicate the dependencies between parts of the state vector and transition groups. Benchmarks on some large case studies, show that the method speeds up the instantiation significantly and decreases memory usage drastically
Generating and Solving Symbolic Parity Games
We present a new tool for verification of modal mu-calculus formulae for
process specifications, based on symbolic parity games. It enhances an existing
method, that first encodes the problem to a Parameterised Boolean Equation
System (PBES) and then instantiates the PBES to a parity game. We improved the
translation from specification to PBES to preserve the structure of the
specification in the PBES, we extended LTSmin to instantiate PBESs to symbolic
parity games, and implemented the recursive parity game solving algorithm by
Zielonka for symbolic parity games. We use Multi-valued Decision Diagrams
(MDDs) to represent sets and relations, thus enabling the tools to deal with
very large systems. The transition relation is partitioned based on the
structure of the specification, which allows for efficient manipulation of the
MDDs. We performed two case studies on modular specifications, that demonstrate
that the new method has better time and memory performance than existing PBES
based tools and can be faster (but slightly less memory efficient) than the
symbolic model checker NuSMV.Comment: In Proceedings GRAPHITE 2014, arXiv:1407.767
Quasipolynomial Set-Based Symbolic Algorithms for Parity Games
Solving parity games, which are equivalent to modal -calculus model
checking, is a central algorithmic problem in formal methods. Besides the
standard computation model with the explicit representation of games, another
important theoretical model of computation is that of set-based symbolic
algorithms. Set-based symbolic algorithms use basic set operations and one-step
predecessor operations on the implicit description of games, rather than the
explicit representation. The significance of symbolic algorithms is that they
provide scalable algorithms for large finite-state systems, as well as for
infinite-state systems with finite quotient. Consider parity games on graphs
with vertices and parity conditions with priorities. While there is a
rich literature of explicit algorithms for parity games, the main results for
set-based symbolic algorithms are as follows: (a) an algorithm that requires
symbolic operations and symbolic space; and (b) an improved
algorithm that requires symbolic operations and symbolic
space. Our contributions are as follows: (1) We present a black-box set-based
symbolic algorithm based on the explicit progress measure algorithm. Two
important consequences of our algorithm are as follows: (a) a set-based
symbolic algorithm for parity games that requires quasi-polynomially many
symbolic operations and symbolic space; and (b) any future improvement
in progress measure based explicit algorithms imply an efficiency improvement
in our set-based symbolic algorithm for parity games. (2) We present a
set-based symbolic algorithm that requires quasi-polynomially many symbolic
operations and symbolic space. Moreover, for the important
special case of , our algorithm requires only polynomially many
symbolic operations and poly-logarithmic symbolic space.Comment: Published at LPAR-22 in 201
Bringing Model Checking Closer To Practical Software Engineering
Bal, H.E. [Promotor]Templon, J.A. [Copromotor]Willemse, T.A.C. [Copromotor