Abstract
The state complexity of a language L is the number of states of its minimal automaton. The operational (state) complexity of an operation \(\otimes \) is the maximal state complexity of the languages \(\otimes (L_1,\cdots ,L_k)\) where \(L_1,\dots ,L_k\) are languages with given state complexities. We highlight two tools that have been developed in recent years and which theorise and summarise a large number of methods used since the 1970 s. A monster is an automaton where any function from states to states is represented by at least one letter. A modifier is a set of functions that allows us to transform a set of automata into an automaton. Thanks to these techniques, we revisit the state complexity of some language transformation operations. These tools allow to propose a unified vision on a large number of known results in a natural way and also to obtain new ones at lower cost. We detail two examples: the star of a boolean operation and the reversal of a boolean operation. By doing so, we directly obtain a new result when the boolean operation is the Xor.
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Caron, P., Luque, JG., Patrou, B. (2023). Operational State Complexity Revisited: The Contribution of Monsters and Modifiers. In: Bordihn, H., Tran, N., Vaszil, G. (eds) Descriptional Complexity of Formal Systems. DCFS 2023. Lecture Notes in Computer Science, vol 13918. Springer, Cham. https://doi.org/10.1007/978-3-031-34326-1_1
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