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Decremental Subset Construction

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Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 72))

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Abstract

Finite automata are exploited in several domains, including language processing, artificial intelligence, automatic control, and software engineering. Let \({\mathcal {N}}\) be a nondeterministic finite automaton (NFA) and \({\mathcal {D}}\) the deterministic finite automaton (DFA) equivalent to \({\mathcal {N}}\). Assume to decrement \({\mathcal {N}}\) by \(\varDelta {\mathcal {N}}\), thus obtaining the decremented NFA \({\mathcal {N}}' = {\mathcal {N}}\setminus \varDelta {\mathcal {N}}\), where some states and/or transitions are missing. Consider determinizing \({\mathcal {N}}'\). Instead of determinizing \({\mathcal {N}}'\) from scratch using the classical Subset Construction algorithm, we propose Decremental Subset Construction, an algorithm which generates the DFA \({\mathcal {D}}'\) equivalent to \({\mathcal {N}}'\) by updating \({\mathcal {D}}\) based on \({\mathcal {N}}\) and \(\varDelta {\mathcal {N}}\). This way, only the actions necessary to transform \({\mathcal {D}}\) into \({\mathcal {D}}'\) are applied. Although evidence from worst-case complexity analysis indicates that Decremental Subset Construction is not better than Subset Construction, in practice, when \({\mathcal {N}}\) is large and \(\varDelta {{\mathcal {N}}}\) relatively small, Decremental Subset Construction may outperform Subset Construction significantly.

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Notes

  1. 1.

    Because of possible merging of states by auxiliary procedure Merge (Algorithm 5), several transitions marked by the same symbol may exit the same state in \({\mathcal {D}}\). However, such nondeterminism in \({\mathcal {D}}\) disappears before ending the processing of buds, as guaranteed by Theorem 1.

  2. 2.

    Retaining the empty state until \({\mathcal {B}}= \emptyset \) is essential in order to avoid the disconnection of \({\mathcal {D}}\).

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Acknowledgment

This work was supported in part by Zhejiang Provincial Natural Science Foundation of China (No. LY16F020004); National Natural Science Foundation of China (No. 61472369).

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Correspondence to Gianfranco Lamperti .

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Lamperti, G., Zhao, X. (2018). Decremental Subset Construction. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-59421-7_3

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