Advanced AI Algorithms for Scalable Trust Management in Multi-Agent Robotic Swarm Systems

Abstract

In recent years, the development of multi-agent robotic swarm systems has become a significant area of research, particularly in applications where decentralized decision-making and autonomous collaboration are critical. One of the central challenges in these systems is establishing scalable trust management mechanisms that ensure the reliability, security, and effective collaboration of agents within the swarm. This paper explores advanced artificial intelligence (AI) algorithms that address this challenge by providing scalable, robust, and adaptive trust management solutions. The paper discusses various trust models, including reputation-based systems, Bayesian networks, and machine learning approaches, and examines their application in multi-agent systems (MAS) for tasks such as coordination, fault tolerance, and decision-making. Furthermore, the paper analyzes the trade-offs between scalability and complexity in AI-driven trust management models, emphasizing their potential in real-world swarm robotic systems. By synthesizing existing research and highlighting future research directions, this paper aims to provide a comprehensive overview of AI algorithms that can enable scalable trust management in robotic swarms, ultimately fostering more efficient and reliable collaborative robotic systems.