Abstract
We study position sampling in a 2-way nondeterministic finite automaton (2NFA) to measure the information dependency and information flow between state variables, based on the information-theoretic sampling technique proposed in [16]. We prove that for a 2NFA, the language generated by position sampling is regular. We also show that for a 2NFA, we can effectively find a vector of sampling positions that maximizes dependency and information flow in a run of the 2NFA. Finally, we give some language properties of sampled runs of 2NFAs augmented with restricted unbounded storage.
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Dang, Z., Ibarra, O.H., Lin, Q. (2015). Sampling a Two-Way Finite Automaton. In: Adamatzky, A. (eds) Automata, Universality, Computation. Emergence, Complexity and Computation, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-09039-9_4
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DOI: https://doi.org/10.1007/978-3-319-09039-9_4
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