System Model Formulation Using Markov Chains

نویسندگان

  • Sam Snodgrass
  • David W. Aha
چکیده

Creating formal models for systems manually is time consuming and difficult. Automating the generation and verification of these formal models can reduce the overhead of developing the models. In this paper we propose an approach (MC-MC) to verifying and generating portions of the formal model using Markov chains. Learning Formal Models of Systems Formal models are used to explain the required behavior of a system. However, manually creating these models is difficult and time consuming. Machine learning techniques can be used to automatically learn, generate, and verify portions of the model, lessening the burden on the designers and developers. An important part of a formal model is a diagram showing the different states of the system, and how each state can be reached (or not reached) from another. These diagrams are similar to state machines or automata, and there is a large corpus of work related to automatic generation of automata through learning. For example, Balle et al. (Balle et al. 2013) use a spectral learning algorithm to learn weighted automata. Additionally, Cleeremans et al. (Cleeremans, ServanSchreiber, and McClelland 1989) and Giles et al. (Giles et al. 1992) explore the use of neural networks for learning state machines. However, these approaches have been used primarily for grammar modeling, and have not been applied to constructing a formal model of a system. Additionally, some research has been performed on automating and aiding in the design and verification of formal systems. Păsăreanu et al. (Păsăreanu et al. 2008) employ the L∗ machine learning algorithm to perform system verification. We are instead interested in the problem of model generation, which needs to precede verification. Neema et al. (Neema et al. 2003) introduce a mixed-initiative toolset for aiding in model design, but we want to automatically generate models in order to ease the burden of the designers and developers. There has also been some work on using Markov chains to test formal models (Whittaker, Thomason, and others 1994) and on learning the transition weights for the automaton style models, given the structure (Whittaker and Poore 1993), but not on using Markov chains to learn the structure of the models themselves. The work we present in this paper is closely related to the above work on learning state machines and their associated transition weights. We describe an application of Markov chains to an interesting domain: learning a state machine from a set of traces of system behavior in order to model a system.

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تاریخ انتشار 2014