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MODEL CHECKING AND STATE MACHINES

Transition systems

Publication year

Between 2000 and 2022

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20 resources

Bartels, F., Sokolova, A., & de Vink, E. (2003). A hierarchy of probabilistic system types. Electronic Notes in Theoretical Computer Science, 82(1), 57–75. https://doi.org/10/d7kq38
Boutillier, P., Maasha, M., Li, X., Medina-Abarca, H. F., Krivine, J., Feret, J., … Fontana, W. (2018). The Kappa platform for rule-based modeling. Bioinformatics, 34(13), i583–i592. https://doi.org/10/gdrhw6
Dal Lago, U., & Hoshino, N. (2019). The Geometry of Bayesian Programming (pp. 1–13). https://doi.org/10/ggdk85
Desharnais, J., Edalat, A., & Panangaden, P. (2002). Bisimulation for Labelled Markov Processes. Information and Computation, 179(2), 163–193. https://doi.org/10/fmp9vd
Dong, H., Mao, J., Lin, T., Wang, C., Li, L., & Zhou, D. (2019). Neural Logic Machines. ArXiv:1904.11694 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1904.11694
Fages, F., Calzone, L., & Soliman, S. (2006). BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics, 22(14), 1805–1807. https://doi.org/10/dfv
Fages, F. (2014). Cells as Machines: Towards Deciphering Biochemical Programs in the Cell. In R. Natarajan (Ed.), Distributed Computing and Internet Technology (pp. 50–67). Cham: Springer International Publishing. https://doi.org/10/ggdf96
Fages, F., Calzone, L., Chabrier-Rivier, N., & Soliman, S. (2006). Machine Learning Biochemical Networks from Temporal Logic Properties. In C. Priami & G. Plotkin (Eds.), Transactions on Computational Systems Biology VI (pp. 68–94). Berlin, Heidelberg: Springer. https://doi.org/10/dd8
Fages, F., Martinez, T., Rosenblueth, D. A., & Soliman, S. (2018). Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 15(4), 1138–1151. https://doi.org/10/ggdf94
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. ArXiv:1410.5401 [Cs]. Retrieved from http://arxiv.org/abs/1410.5401
Murfet, D. (2019). dmurfet/2simplicialtransformer. Retrieved from https://github.com/dmurfet/2simplicialtransformer (Original work published 2019)
Murfet, D., & Clift, J. (2019). Derivatives of Turing machines in Linear Logic. ArXiv:1805.11813 [Math]. Retrieved from http://arxiv.org/abs/1805.11813
Murfet, D., Clift, J., Doryn, D., & Wallbridge, J. (2019). Logic and the $2$-Simplicial Transformer. ArXiv:1909.00668 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1909.00668
Panangaden, P. (2009). Labelled Markov Processes. London, UK, UK: Imperial College Press.
Rutten, J. J. M. M. (2000). Universal coalgebra: a theory of systems. Theoretical Computer Science, 249(1), 3–80. https://doi.org/10/fqrjpn
Serafini, L., & Garcez, A. d’Avila. (2016). Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge. ArXiv:1606.04422 [Cs]. Retrieved from http://arxiv.org/abs/1606.04422
Sokolova, A. (2011). Probabilistic systems coalgebraically: A survey. Theoretical Computer Science, 412(38), 5095–5110. https://doi.org/10/frbx24
Sokolova, A., & de Vink, E. P. (2004). Probabilistic Automata: System Types, Parallel Composition and Comparison. In C. Baier, B. R. Haverkort, H. Hermanns, J.-P. Katoen, & M. Siegle (Eds.), Validation of Stochastic Systems (Vol. 2925, pp. 1–43). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-24611-4_1
van Breugel, F., Mislove, M., Ouaknine, J., & Worrell, J. (2005). Domain theory, testing and simulation for labelled Markov processes. Theoretical Computer Science, 333(1), 171–197. https://doi.org/10/ft9vc5
Wilkinson, D. J. (2006). Stochastic Modelling for Systems Biology. CRC Press.

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