We present CGAAL, our efficient on-the-fly model checker for alternating-time temporal logic (ATL) on concurrent game structures (CGS).
This paper is available on Arxiv under CC 4.0 license. Authors: Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & [email protected]; Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & [email protected]; Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & [email protected]; Jener Rasmussen, [email protected]; Mathias M. Sørensen, [email protected]; Asger G.
’s documentation and among case studies we have developed. In our evaluation, we also compare and evaluate our search strategies, and find that our custom search strategies are often significantly faster than the usual breadth-first and depth-first search strategies. 1 Introduction Software plays a large role in our everyday lives, making decisions, enabling efficient communication, ensuring safety, and many more critical tasks.
, both symbolic and explicit state. The , a model checker implemented in Rust that allows for the verification of ATL properties on concurrent games. -games. Though while and the LCGS language. An evaluation of our tool and a comparison with -games on case studies from excels at stochastic models such as discreteand continuous-time Markov chains, Markov decision processes, and probabilistic timed automata, for which it can verify various properties described in LTL , CSL, and probabilistic CTL*. There are multiple verification engines in
-games added support for concurrent stochastic games. In , Liu and Smolka present a global and a local algorithm to compute fixed-point boolean vertex assignments in dependency graphs with directed hyper-edges representing dependencies between vertex values. The global algorithm has a better worst-case running time of the two, but the on-the-fly local algorithm only explores the graph as needed to compute the fixed-point assignment. Many problems have since been encoded as dependency graphs.
implements a variety of strategies for searching the problem state space: a heuristic inspired by PageRank and two heuristic search strategies that exploit the state vector representation of our model, alongside the usual breadth-first and depth-first approach. We conduct experiments comparing our different search strategies with each other on several case studies and show that having more available compute threads often leads to speed-ups of 1-2 orders of magnitude.
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