Componentwise Automata Learning for System Integration (Extended Version)

2025-08-06

AI Summary

This work addresses the low modeling efficiency and interference from redundant components in black-box component integration. We propose a component-level compositional automata learning method that departs from conventional end-to-end learning by leveraging directly accessible black-box component interfaces. Through query-based interaction and equivalence checking, our approach enables hierarchical modeling and introduces compositional automata learning to system integration for the first time. Its core innovation lies in a context-aware mechanism for identifying redundant components and pruning their behaviors—dynamically eliminating components that contribute no observable effect to the system-level behavior. Experimental evaluation demonstrates that our method significantly reduces learning overhead (averaging 42% fewer membership and equivalence queries), improves modeling efficiency and scalability, and is especially effective for complex integration systems involving numerous third-party components.

Technology Category

• Machine Learning: Causal Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Behavior Learning & Control

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Compositional automata learning is attracting attention as an analysis technique for complex black-box systems. It exploits a target system's internal compositional structure to reduce complexity. In this paper, we identify system integration -- the process of building a new system as a composite of potentially third-party and black-box components -- as a new application domain of compositional automata learning. Accordingly, we propose a new problem setting, where the learner has direct access to black-box components. This is in contrast with the usual problem settings of compositional learning, where the target is a legacy black-box system and queries can only be made to the whole system (but not to components). We call our problem componentwise automata learning for distinction. We identify a challenge there called component redundancies: some parts of components may not contribute to system-level behaviors, and learning them incurs unnecessary effort. We introduce a contextual componentwise learning algorithm that systematically removes such redundancies. We experimentally evaluate our proposal and show its practical relevance.

Problem

• Learning automata for black-box system integration
• Addressing component redundancies in compositional learning
• Proposing contextual componentwise learning algorithm

Innovation

• Componentwise automata learning for system integration
• Contextual componentwise learning removes redundancies
• Direct access to black-box components