ABSTRACT
We present a flexible and efficient toolchain to symbolically solve (standard) Rabin games, fair-adversarial Rabin games, and 21/2-player Rabin games. To our best knowledge, our tools are the first ones to be able to solve these problems. Furthermore, using the optimized game solvers as back-end, we implement a tool for computing correct-by-construction controllers for stochastic dynamical systems with LTL specifications. An important feature of our toolchain is the flexibility created through two programming abstractions: one separates the symbolic fixpoint computations from the predecessor calculations, and the other one allows effortless switching between different BDD libraries. We empirically compare the benefits of using the CUDD and Sylvan BDD libraries, and report substantial computational savings of our tool compared to the state-of-the-art.
- Tamajit Banerjee, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck, and Sadegh Soudjani. 2022. A direct symbolic algorithm for solving stochastic Rabin games. In TACAS ’22. Springer, 81–98.Google Scholar
- Tamajit Banerjee, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck, and Sadegh Soudjani. 2023. Fast symbolic algorithms for omega-regular games under strong transition fairness. TheoretiCS 2 (2023).Google Scholar
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- Rupak Majumdar, Kaushik Mallik, and Sadegh Soudjani. 2020. Symbolic controller synthesis for Büchi specifications on stochastic systems. In HSCC ’20. 1–11.Google Scholar
- Fabio Somenzi. 2015. CUDD: CU decision diagram package release 3.0.0. URL: https://github.com/ivmai/cudd (2015).Google Scholar
- Tom Van Dijk and Jaco Van De Pol. 2015. Sylvan: Multi-core decision diagrams. In TACAS ’15. Springer, 677–691.Google Scholar
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