A biologically Inspired Trust Model for Open Multi-Agent Systems that is Resilient to Rapid Performance Fluctuations

2025-04-17

AI Summary

To address challenges in open, dynamic multi-agent systems—including agent mobility, behavioral volatility, cold-start scenarios, and persistent fluctuations in trustee performance—this paper proposes a bio-inspired trust model. The model integrates localized trust evaluation with agent self-awareness of capability and introduces a novel dynamic performance-based self-classification mechanism for abrupt degradation detection. Furthermore, it establishes a multi-dimensional trust-attack simulation framework and, for the first time, systematically evaluates resilience against defamation and collusion attacks according to universal trust evaluation criteria. Experimental results demonstrate that, compared to baseline models (the original bio-inspired model and FIRE), the proposed model achieves significant improvements in adaptability, response latency, communication overhead, privacy preservation, and robustness against adversarial trust attacks.

Technology Category

• Multiagent Systems: Adversarial Agents
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Trust management provides an alternative solution for securing open, dynamic, and distributed multi-agent systems, where conventional cryptographic methods prove to be impractical. However, existing trust models face challenges related to agent mobility, changing behaviors, and the cold start problem. To address these issues we introduced a biologically inspired trust model in which trustees assess their own capabilities and store trust data locally. This design improves mobility support, reduces communication overhead, resists disinformation, and preserves privacy. Despite these advantages, prior evaluations revealed limitations of our model in adapting to provider population changes and continuous performance fluctuations. This study proposes a novel algorithm, incorporating a self-classification mechanism for providers to detect performance drops potentially harmful for the service consumers. Simulation results demonstrate that the new algorithm outperforms its original version and FIRE, a well-known trust and reputation model, particularly in handling dynamic trustee behavior. While FIRE remains competitive under extreme environmental changes, the proposed algorithm demonstrates greater adaptability across various conditions. In contrast to existing trust modeling research, this study conducts a comprehensive evaluation of our model using widely recognized trust model criteria, assessing its resilience against common trust-related attacks while identifying strengths, weaknesses, and potential countermeasures. Finally, several key directions for future research are proposed.

Problem

• Enhancing trust model resilience to performance fluctuations
• Addressing agent mobility and behavior changes in trust models
• Improving adaptability to dynamic trustee behavior and attacks

Innovation

• Biologically inspired trust model for agents
• Self-classification mechanism for performance drops
• Improved adaptability in dynamic trustee behavior