ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation

2026-04-24

AI Summary

This work addresses the lack of explicit, auditable conflict resolution mechanisms in existing multi-agent requirement negotiation approaches, which hinders compliance with transparency demands in highly regulated contexts. The paper introduces Dung’s abstract argumentation framework into requirements engineering for the first time, modeling proposals, critiques, and refinements as argument nodes, with attack relations capturing conflicts. Acceptable argument sets are derived using grounded and preferred semantics, integrated within a structured negotiation process that combines KAOS goal modeling and multi-layer validation. The approach provides argument-level traceability and enables automatic generation of standards-compliant artifacts. Empirical results demonstrate significantly superior traceability over baselines, higher decision rationality (4.32 vs. 3.07, p<0.001), 94.9% BERTScore semantic retention, and improved compliance coverage at 84.7% compared to baseline ranges of 47.6%–47.8%.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

As software systems grow in complexity, they must satisfy an increasing number of competing quality attributes, making it essential to balance them in a principled manner -- for example, a safety requirement for sensor-fusion verification may conflict with a tight planning-cycle budget. Multi-agent large language model frameworks support this balancing process by assigning specialized agents to different objectives. However, their conflict resolution is typically heuristic. Requirements are aggregated implicitly without explicit acceptance or rejection, limiting auditability in regulated domains. We present ArgRE, a multi-agent requirements negotiation system that embeds Dung-style abstract argumentation into the negotiation stage. Each proposal, critique, and refinement is modeled as an argument, conflicts are represented as directed attack relations, and the accepted set of arguments is computed under grounded and preferred semantics. The pipeline further integrates KAOS goal modeling, multi-layer verification, and standards-oriented artifact generation. Evaluation across five case studies spanning safety-critical, financial, and information-system domains shows that ArgRE provides argument-level traceability absent from existing frameworks. Independent evaluators rated its decision justifications significantly higher than those of heuristic synthesis (4.32 vs. 3.07, p < 0.001), indicating improved auditability, while semantic intent preservation remains comparable (94.9% BERTScore F1) and compliance coverage reaches 84.7% versus 47.6%--47.8% for baselines. Structural analysis further confirms that the default pairwise protocol yields acyclic graphs in which grounded and preferred semantics coincide, whereas cross-pair arbitration introduces controlled cyclicity, leading to predictable divergence between the two semantics.

Problem

• Conflict Resolution
• Multi-Agent Requirements Negotiation
• Argumentation
• Auditability
• Requirements Engineering

Innovation

• abstract argumentation
• multi-agent negotiation
• requirements engineering
• auditability
• conflict resolution