Abstract
AbstractWe present a compositional model checking algorithm for Markov decision processes, in which they are composed in the categorical graphical language ofstring diagrams. The algorithm computes optimal expected rewards. Our theoretical development of the algorithm is supported by category theory, while what we call decomposition equalities for expected rewards act as a key enabler. Experimental evaluation demonstrates its performance advantages.
Publisher
Springer Nature Switzerland
Reference21 articles.
1. Baier, C., Katoen, J.: Principles of Model Checking. MIT Press, Cambridge (2008)
2. Lecture Notes in Computer Science;C Baier,2017
3. Bonchi, F., Holland, J., Piedeleu, R., Sobocinski, P., Zanasi, F.: Diagrammatic algebra: from linear to concurrent systems. Proc. ACM Program. Lang. 3(POPL), 25:1–25:28 (2019). https://doi.org/10.1145/3290338
4. Clarke, E.M., Long, D.E., McMillan, K.L.: Compositional model checking. In: Proceedings of the Fourth Annual Symposium on Logic in Computer Science (LICS ’89), Pacific Grove, California, USA, 5–8 June 1989, pp. 353–362. IEEE Computer Society (1989). https://doi.org/10.1109/LICS.1989.39190
5. Cruttwell, G.S.: Normed spaces and the change of base for enriched categories. Ph.D. thesis, Dalhousie University (2008)
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