Learning DFAs from Positive Examples Only via Word Counting

TL;DR

This paper investigates the challenge of learning finite automata solely from positive examples by analyzing word counts, proving NP-completeness, and proposing a new algorithm with improved asymptotic runtime.

Contribution

It introduces the first complexity analysis for positive-only DFA learning and proposes a novel algorithm with better asymptotic performance.

Findings

Computing minimal accepted words up to a certain length is NP-complete.

The new algorithm has better asymptotic runtime than existing methods.

Experimental results show the algorithm's potential as a preprocessing step.

Abstract

Learning finite automata from positive examples has recently gained attention as a powerful approach for understanding, explaining, analyzing, and verifying black-box systems. The motivation for focusing solely on positive examples arises from the practical limitation that we can only observe what a system is capable of (positive examples) but not what it cannot do (negative examples). Unlike the classical problem of passive DFA learning with both positive and negative examples, which has been known to be NP-complete since the 1970s, the topic of learning DFAs exclusively from positive examples remains poorly understood. This paper introduces a novel perspective on this problem by leveraging the concept of counting the number of accepted words up to a carefully determined length. Our contributions are twofold. First, we prove that computing the minimal number of words up to this length accepted by DFAs of a given size that accept all positive examples is NP-complete, establishing that learning from positive examples alone is computationally demanding. Second, we propose a new learning algorithm with a better asymptotic runtime that the best-known bound for existing algorithms. While our experimental evaluation reveals that this algorithm under-performs state-of-the-art methods, it demonstrates significant potential as a preprocessing step to enhance existing approaches.