Active coevolutionary learning of deterministic finite automata

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Active coevolutionary learning of deterministic finite automata (2005)

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by Josh Bongard , Hod Lipson , Stefan Wrobel

Venue:	Journal of Machine Learning Research
Citations:	36 - 9 self

BibTeX

@ARTICLE{Bongard05activecoevolutionary,
    author = {Josh Bongard and Hod Lipson and Stefan Wrobel},
    title = {Active coevolutionary learning of deterministic finite automata},
    journal = {Journal of Machine Learning Research},
    year = {2005},
    volume = {6},
    pages = {2005}
}

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Abstract

This paper describes an active learning approach to the problem of grammatical inference, specifically the inference of deterministic finite automata (DFAs). We refer to the algorithm as the estimation-exploration algorithm (EEA). This approach differs from previous passive and active learning approaches to grammatical inference in that training data is actively proposed by the algorithm, rather than passively receiving training data from some external teacher. Here we show that this algorithm outperforms one version of the most powerful set of algorithms for grammatical inference, evidence driven state merging (EDSM), on randomly-generated DFAs. The performance increase is due to the fact that the EDSM algorithm only works well for DFAs with specific balances (percentage of positive labelings), while the EEA is more consistent over a wider range of balances. Based on this finding we propose a more general method for generating DFAs to be used in the development of future grammatical inference algorithms.

Keyphrases

deterministic finite automaton active coevolutionary learning grammatical inference active learning approach previous passive approach differs specific balance randomly-generated dfas state merging wider range general method future grammatical inference algorithm performance increase training data external teacher powerful set estimation-exploration algorithm positive labelings

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