Learning deterministic finite automata from interleaved strings

Authors:
Joshua Jones;Tim Oates
Affiliations:
University of Maryland, Baltimore County, Baltimore, Maryland;University of Maryland, Baltimore County, Baltimore, Maryland
Venue:
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Year:
2010

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Abstract

Workflows are an important knowledge representation used to understand and automate processes in diverse task domains. Past work has explored the problem of learning workflows from traces of processing. In this paper, we are concerned with learning workflows from interleaved traces captured during the concurrent processing of multiple task instances. We first present an abstraction of the problem of recovering workflows from interleaved example traces in terms of grammar induction.We then describe a two-stage approach to reasoning about the problem, highlighting some negative results that demonstrate the need to work with a restricted class of languages. Finally, we give an example of a restricted language class called terminated languages for which an accepting deterministic finite automaton (DFA) can be recovered in the limit from interleaved strings, and make remarks about the applicability of the two-stage approach to terminated languages.