Academic

Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry

arXiv:2604.00319v1 Announce Type: new Abstract: We develop algorithms for collaborative control of AI agents and critics in a multi-actor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to complete multimodal tasks such as fault detection, severity, and cause analysis in a network telemetry system, text-to-image generation, video generation, healthcare diagnostics from medical images and patient records, etcetera. The AI agents complete their tasks and send them to AI critics for evaluation. The critics then send feedback to agents to improve their responses. Collaboratively, they minimize the overall cost to the system with no inter-agent or inter-critic communication. AI agents and critics keep their cost functions or derivatives of cost functions private. Using multi-time scale stochastic approximation techniques, we provide c

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Syed Eqbal Alam, Zhan Shu · · 1 min read · 14 views
#cs.AI #cs.MA

arXiv:2604.00319v1 Announce Type: new Abstract: We develop algorithms for collaborative control of AI agents and critics in a multi-actor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to complete multimodal tasks such as fault detection, severity, and cause analysis in a network telemetry system, text-to-image generation, video generation, healthcare diagnostics from medical images and patient records, etcetera. The AI agents complete their tasks and send them to AI critics for evaluation. The critics then send feedback to agents to improve their responses. Collaboratively, they minimize the overall cost to the system with no inter-agent or inter-critic communication. AI agents and critics keep their cost functions or derivatives of cost functions private. Using multi-time scale stochastic approximation techniques, we provide convergence guarantees on the time-average active states of AI agents and critics. The communication overhead is a little on the system, of the order of $\mathcal{O}(m)$, for $m$ modalities and is independent of the number of AI agents and critics. Finally, we present an example of fault detection, severity, and cause analysis in network telemetry and thorough evaluation to check the algorithm's efficacy.

Executive Summary

This article proposes an innovative approach to collaborative AI agents and critics for fault detection and cause analysis in network telemetry. The algorithm utilizes a multi-actor, multi-critic federated multi-agent system, where AI agents and critics access classical machine learning or generative AI foundation models. The AI agents and critics collaborate to complete multimodal tasks with minimal communication overhead. Convergence guarantees are provided using multi-time scale stochastic approximation techniques. The article presents a thorough evaluation of the algorithm's efficacy in fault detection, severity, and cause analysis in network telemetry. While the approach shows promise, its scalability and adaptability to real-world applications remain to be explored.

Key Points

  • ▸ Collaborative AI agents and critics utilize a multi-actor, multi-critic federated multi-agent system.
  • ▸ AI agents and critics access classical machine learning or generative AI foundation models.
  • ▸ Minimal communication overhead is achieved through multi-time scale stochastic approximation techniques.

Merits

Strength in Task-Specific Performance

The algorithm demonstrates strong performance in fault detection, severity, and cause analysis in network telemetry, with potential applications in other multimodal tasks.

Scalability and Adaptability

The approach allows for decentralized decision-making, potentially enabling the algorithm to scale to complex, real-world applications with minimal communication overhead.

Demerits

Limited Explorations of Real-World Applications

While the algorithm shows promise, its adaptability and scalability to real-world applications, such as healthcare diagnostics and text-to-image generation, require further exploration.

Potential Vulnerabilities to Adversarial Attacks

The decentralized nature of the algorithm may leave it vulnerable to adversarial attacks, which could compromise the system's performance and security.

Expert Commentary

The proposed algorithm represents a significant advancement in the development of collaborative AI agents and critics for multimodal tasks. While the article presents a thorough evaluation of the algorithm's efficacy in fault detection and cause analysis in network telemetry, further research is needed to explore its scalability and adaptability to real-world applications. Additionally, the decentralized nature of the algorithm raises important questions about data ownership, privacy, and security, which policymakers and regulatory bodies should address in developing guidelines for the use of collaborative AI agents and critics.

Recommendations

  • ✓ Recommendation 1: Future research should focus on exploring the algorithm's scalability and adaptability to real-world applications, including healthcare diagnostics and text-to-image generation.
  • ✓ Recommendation 2: Policymakers and regulatory bodies should develop guidelines for the use of collaborative AI agents and critics, addressing issues related to data ownership, privacy, and security.

Sources

Original: arXiv - cs.AI

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