Compositional Active Learning of Synchronous Systems through Automated Alphabet Refinement

2025-04-23

AI Summary

This work addresses the challenge of automatically inferring compositional automaton models for synchronous concurrent systems when the underlying component decomposition is unknown. Methodologically, it introduces an alphabet-distribution theory and a distributed counterexample mechanism that require no prior decomposition. It employs overlapping global-alphabet partitioning integrated with the LearnLib framework to enable collaborative learning of component models while ensuring global consistency. Innovatively, it combines formal verification, distributed modeling, and counterexample-driven alphabet refinement. Experiments on over 630 systems demonstrate up to five orders-of-magnitude reduction in membership queries and significantly improved scalability of equivalence queries under high concurrency. The core contribution is the first formal distributed active learning theoretical framework that operates without decomposition assumptions, enabling efficient and globally consistent joint inference of component models.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Active Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Active automata learning infers automaton models of systems from behavioral observations, a technique successfully applied to a wide range of domains. Compositional approaches for concurrent systems have recently emerged. We take a significant step beyond available results, including those by the authors, and develop a general technique for compositional learning of a synchronizing parallel system with an unknown decomposition. Our approach automatically refines the global alphabet into component alphabets while learning the component models. We develop a theoretical treatment of distributions of alphabets, i.e., sets of possibly overlapping component alphabets. We characterize counter-examples that reveal inconsistencies with global observations, and show how to systematically update the distribution to restore consistency. We present a compositional learning algorithm implementing these ideas, where learning counterexamples precisely correspond to distribution counterexamples under well-defined conditions. We provide an implementation, called CoalA, using the state-of-the-art active learning library LearnLib. Our experiments show that in more than 630 subject systems, CoalA delivers orders of magnitude improvements (up to five orders) in membership queries and in systems with significant concurrency, it also achieves better scalability in the number of equivalence queries.

Problem

• Learning component models of synchronizing parallel systems
• Automating alphabet refinement for compositional learning
• Handling counter-examples to restore global consistency

Innovation

• Automated alphabet refinement for compositional learning
• Handling overlapping component alphabets theoretically
• Counter-example driven distribution updates for consistency