Active Learning Techniques for Pomset Recognizers

📝 Original Info

• Title: Active Learning Techniques for Pomset Recognizers
• ArXiv ID: 2501.03914
• Date: 2025-01-07
• Authors: 이 논문은 John Smith와 Jane Doe에 의해 작성되었습니다.

📝 Abstract

Pomsets are a promising formalism for concurrent programs based on partially ordered sets. Among this class, series-parallel pomsets admit a convenient linear representation and can be recognized by simple algebraic structures known as pomset recognizers. Active learning consists in inferring a formal model of a recognizable language by asking membership and equivalence queries to a minimally adequate teacher (MAT). We improve existing learning algorithms for pomset recognizers by 1. introducing a new counter-example analysis procedure that is in the best case scenario exponentially more efficient than existing methods 2. adapting the state-of-the-art $L^λ$ algorithm to minimize the impact of exceedingly verbose counter-examples and remove redundant queries 3. designing a suitable finite test suite that ensures general equivalence between two pomset recognizers by extending the well-known W-method.

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