Kristian Guillaumier and John Abela
Link to the paper: here
Abstract: The grammatical inference community has been studying evolutionary methods for DFA learning for almost three decades. These methods typically operate by learning a representation of the target DFA either as a partitioning the states of a prefix tree acceptor or as an encoding of its transition matrix. In this paper, we present an alternative approach for learning random DFAs over binary alphabets from sparse training data. We first conducted several experiments on thousands of problem instances to study their behaviour and to better understand the conditions under which state merging algorithms succeed or fail. Motivated by these observations, we implemented an evolutionary algorithm in which the chromosomes encode short sequences of merges selected from a subset of high state reduction merges. The fitness of a chromosome is then measured by extending it using the EDSM heuristic and the size of the final hypothesis is used to score the entire sequence. To improve runtime performance, we use a method that can reliably estimate the fitness of a sequence of merges without extending it completely. We use the state-of-the-art EDSM algorithm as a baseline to compare our results to and observe that we can find low-error hypotheses or the exact target DFAs with a considerably higher likelihood.
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