Active Learning Techniques for Pomset Recognizers

2025-01-07

AI Summary

This paper addresses the series-parallel pomset recognition problem in concurrent program modeling. To overcome the high query cost and inefficient counterexample handling of conventional approaches—especially on complex, long-structured pomsets—we propose an active learning framework for pomset recognizers. Our contributions are threefold: (1) an exponentially more efficient counterexample analysis algorithm; (2) a refined $L^lambda$ algorithm that suppresses redundant membership queries and equivalent queries; and (3) an extended $W$-method to generate a finite, complete test suite, ensuring reliable equivalence checking. Integrating the Minimally Adequate Teacher (MAT) paradigm, algebraic language recognition, and pomset theory, our framework significantly reduces both query complexity and counterexample processing overhead. In the theoretically optimal case, it achieves exponential speedup, thereby enhancing both the efficiency and practicality of automated pomset inference.

Technology Category

• Machine Learning: Active Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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 $L^{lambda}$ 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.

Problem

• Active Learning Optimization
• Pomset Recognition
• Complex Structure Handling

Innovation

• Improved Active Learning
• Optimized Algorithm
• Comprehensive Testing Scheme