Mutually compatible and incompatible
merges for the search of the smallest consistent DFA
John Abela, Francois Coste, Sandro Spina
<>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.
To appear in the ICGI'04 conference proceedings to be published by
as a volume in the Lecture
Notes in Artificial Intelligence which is part of the Springer-Verlag
Lecture Notes in Computer Science Series.