Cooperative Intelligent Control of Multi-Agent Systems (MAS) Through Communication, Trust, and Reliability

The field of Multi-Agent Systems (MAS) has achieved significant advancements in modern research and development. This study focuses on enhancing trust evaluation, communication efficiency, and adaptive navigation in scenarios where agents have limited prior knowledge. Key contributions include the development of a high-intelligence MAS system that integrates key input data, such as real-time parameters regarding agents’ distances from one another, their distances to target locations, weather conditions, visibility, machine learning capabilities, positions relative to safe or unsafe environments for trust evaluation, delays in communication, and potential cyber threats. These factors trigger a dynamic topology-switching mechanism to secure agents or minimize communication delays in high-security operations. The MAS implements these strategies based on an adaptive communication model, enabling agents to execute various steps during data pooling effectively. Agents utilize real-time data to coordinate flock movements, ensuring dynamic and robust control through data pooling. For example, in a topology requiring a lead agent, the lead agent provides navigation instructions based on pooled data collected during mission execution. This data may involve repositioning proper area coverage, considering agents’ visibility, distance, or environmental disturbances. Four main topologies are implemented in this experiment: directed mesh with two lead agents (type A), directed mesh with one lead agent (type B), star topology (type C), and ring topology (type D). Type B and C topologies are well-suited for communication without delays or disturbances but perform poorly when the system experiences delays (e.g., noise disturbances exceeding a threshold frequency of 5 Hz). In contrast, type A and D topologies are more effective in handling communication delays. By implementing a topology-switching mechanism, this research streamlines the application of two or more topologies in real-life scenarios. It utilizes type B or C topologies in undisturbed conditions and switches to type A or D when perturbations occur. This optimization minimizes communication delays during mission execution and flight time. The research demonstrates significant improvements in trust evaluation, communication efficiency, and overall MAS performance, with implications across various domains, including image and video mining. In these areas, the integration of domain-specific agents enhances processes such as preprocessing, feature extraction, and interpretation. Results show improved accuracy and reliability in data analysis and decision-making across diverse applications, particularly in scenarios involving complex spatial objects and varying environmental conditions.