conference · 2002
Efficient search techniques for the inference of minimum size finite automata
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- TL;DR
- We propose a new algorithm to infer the minimum-size deterministic automaton consistent with a given set of input and output strings.
- Problem
- Finding the minimum-size deterministic automaton consistent with a prespecified set of strings is a challenging computational search problem.
- Method
- Our approach improves a well-known search algorithm by A.W. Bierman and J.A. Feldman (1972) by incorporating dependency-directed backtracking techniques for the first time in this context.
- Results
- For the problems studied, the application of these techniques yields an algorithm that is orders of magnitude faster than existing approaches.
- Contributions
- We are the first to apply dependency-directed backtracking to the problem of inferring minimum size deterministic automata.
- Limitations
- Not specified in the abstract.
- Takeaways
- Dependency-directed backtracking can dramatically improve search efficiency for automaton inference, achieving orders-of-magnitude speedups over existing methods.
- Applications
- Not specified in the abstract.
- Topics
- Not specified in the abstract.
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
We propose a new algorithm for the inference of the minimum size deterministic automaton consistent with a prespecified set of input/output strings. Our approach improves a well known search algorithm proposed by A.W. Bierman and J.A. Feldman (1972), by incorporating a set of techniques known as dependency directed backtracking. These techniques have already been used in other applications, but we are the first to apply them to this problem. The results show that the application of these techniques yields an algorithm that is, for the problems studied, orders of magnitude faster than existing approaches.