Mutually Compatible and Incompatible Merges for the Search of the Smallest Consistent DFA

  • John Abela
  • François Coste
  • Sandro Spina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3264)


State Merging algorithms, such as Rodney Price’s EDSM (Evidence-Driven State Merging) algorithm, have been reasonably successful at solving DFA-learning problems. EDSM, however, often does not converge to the target DFA and, in the case of sparse training data, does not converge at all. In this paper we argue that is partially due to the particular heuristic used in EDSM and also to the greedy search strategy employed in EDSM. We then propose a new heuristic that is based on minimising the risk involved in making merges. In other words, the heuristic gives preference to merges, whose evidence is supported by high compatibility with other merges. Incompatible merges can be trivially detected during the computation of the heuristic. We also propose a new heuristic limitation of the set of candidates after a backtrack to these incompatible merges, allowing to introduce diversity in the search.


State Partition Grammatical Inference Hypothesis Size Training String Mutual Compatibility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • John Abela
    • 1
  • François Coste
    • 2
  • Sandro Spina
    • 1
  1. 1.Department of Computer Science & AIUniversity of MaltaMalta
  2. 2.INRIA/IRISARennes CedexFrance

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