Learning-Based Argumentation Frameworks for Structured Reasoning in Domain-Specific AI

Abstract

AI-driven argumentation models offer a structured approach to automated reasoning by combining symbolic logic representation, domain-grounded knowledge retrieval, and natural language articulation. This study introduces a hybrid framework that enables machine learning systems to generate explicit, traceable argument chains supporting domain-specific decision-making. The methodology incorporates premise–claim scaffolding, state-tracked inference evolution, and structured counterargument generation to ensure coherence across multi-stage analytical workflows. Evaluation results show that the proposed approach improves interpretability and reduces unsupported inference leaps compared to conventional generative models. The system demonstrates effectiveness in both static reasoning tasks and user-interactive deliberation, maintaining logical consistency while enabling adaptive refinement of conclusions. These findings suggest that AI-based reasoning architectures can meaningfully augment expert decision processes in domains requiring transparency, justification, and multi-perspective analysis.