2024/01/23 by Loes Kruger, Kruger, Loes, Sebastian Junges +3 · 3 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Machine Learning and Algorithms #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.2401.12703
openalex publication_date 2024/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A bottleneck in modern active automata learning is to test whether a hypothesized Mealy machine correctly describes the system under learning. The search space for possible counterexamples is given by so-called test suites, consisting of input sequences that have to be checked to decide whether a counterexample exists. This paper shows that significantly smaller test suites suffice under reasonable assumptions on the structure of the black box. These smaller test suites help to refute false hypotheses during active automata learning, even when the assumptions do not hold. We combine multiple test suites using a multi-armed bandit setup that adaptively selects a test suite. An extensive empirical evaluation shows the efficacy of our approach. For small to medium-sized models, the performance gain is limited. However, the approach allows learning models from large, industrial case studies that were beyond the reach of known methods.