Disagree and Commit: Degrees of Argumentation-based Agreements

2024-12-31

AI Summary

This paper addresses the challenge of achieving reliable consensus among multi-agent systems operating in dynamic environments. Methodologically, it introduces a novel partial-consensus modeling and evaluation framework: (1) formally defining satisfaction and three agreement metrics—minimum, mean, and median agreement degrees; (2) constructing a quantifiable model of value influence; and (3) integrating formal logic with metric analysis within an abstract and value-based argumentation framework to yield a computable agreement assessment tool. Key contributions include: (1) enabling commitment maintenance and decision progression under non-unanimous agreement; (2) providing theoretical proof that partial consensus exhibits robustness under incremental information updates; and (3) integrating the tool into an open-source argumentation reasoning library. Empirical evaluation demonstrates that the framework significantly enhances the naturalness and robustness of multi-agent coordination.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Knowledge Representation and Reasoning: Argumentation
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In cooperative human decision-making, agreements are often not total; a partial degree of agreement is sufficient to commit to a decision and move on, as long as one is somewhat confident that the involved parties are likely to stand by their commitment in the future, given no drastic unexpected changes. In this paper, we introduce the notion of agreement scenarios that allow artificial autonomous agents to reach such agreements, using formal models of argumentation, in particular abstract argumentation and value-based argumentation. We introduce the notions of degrees of satisfaction and (minimum, mean, and median) agreement, as well as a measure of the impact a value in a value-based argumentation framework has on these notions. We then analyze how degrees of agreement are affected when agreement scenarios are expanded with new information, to shed light on the reliability of partial agreements in dynamic scenarios. An implementation of the introduced concepts is provided as part of an argumentation-based reasoning software library.

Problem

• Robot consensus
• Group decision-making
• Reliability assessment

Innovation

• Consensus Algorithm
• Dynamic Stability
• Argumentation-based Decision Support