Distributed Case-Based Reasoning in Multi-Agent Systems for Adaptive Communication Modeling in the Digital Era

Multi-Agent Systems (MAS) functioning in the dynamic, heterogeneous settings of the digital age require adaptive communication models. Scalability, dynamism, and context awareness are problems for static protocols and centralized reasoning. In order to facilitate reliable, flexible communication, this research suggests a unique architecture that incorporates Distributed Case-Based Reasoning (DCBR) within MAS. In order to capture previous effective communication tactics contextualized by network status, agent responsibilities, and job needs, agents cooperatively manage distributed case bases. The methodology describes a structured DCBR lifecycle that includes context-aware retention, distributed revision based on outcome feedback, collaborative case reuse and adaptation for strategy creation, and distributed case retrieval that makes advantage of local similarity and query propagation. Coordination amongst agents during reasoning is facilitated by a specific communication protocol. The system is tested against rule-based MAS and centralized CBR baselines in a simulated dynamic service composition scenario. Significant superiority is shown by key measures such as communication success rate, latency reduction (avg. 32%), adaption convergence time (avg. 45% faster), and scalability under growing agents/cases. The framework offers a scalable, learning-based method for adaptive interaction, and the results validate its efficacy in managing the diverse agents and dynamic environments common in contemporary digital systems (e.g., IoT, ad-hoc networks).