Adaptive Protocol Synthesis for Emergent Anomalies in Multi-Agent Communication

Most current works, like Goeckner et al.’s MAGEC [Source 1, Heuristic 1], focus on resilience against predefined anomalies (e.g., agent attrition, communication loss). However, real-world environments can give rise to entirely new or compound anomalies that were not foreseen during training or design. This research proposes a protocol synthesis engine embedded within the MAS that continually monitors communication patterns, detects deviations from expected protocol behaviors (drawing inspiration from anomaly detection approaches in Shakiba & Beigi [Source 4, Heuristic 1]), and dynamically synthesizes or repairs communication rules on the fly. Unlike elastic observer approaches (Yang et al., Source 2), this system would not just observe but actually alter the communication protocol in response to emergent threats. By integrating online learning, formal verification, and anomaly detection, this approach could create MASs that remain robust even in the face of “unknown unknowns.” The impact is a new class of self-evolving MAS protocols capable of withstanding both anticipated and unprecedented communication challenges.

References:

1. Graph Neural Network-based Multi-agent Reinforcement Learning for Resilient Distributed Coordination of Multi-Robot Systems. Anthony Goeckner, Yueyuan Sui, Nicolas Martinet, Xinliang Li, Qi Zhu (2024). IEEE/RJS International Conference on Intelligent RObots and Systems.
2. A Fully Distributed Resilient Control Strategy Based on Multi-Agent Systems. Xinchi Yang, Ziyue Yao, Zhong Liu, Hongli Zhao, Amei Shen, Yusong Jin (2024). 2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE).
3. Anomaly Detection in Network Traffic using Distributed Self-Organizing Multi Agent Systems. MSc Niloofar Shakiba, Akram Beigi (2021).