Enhancing agent sociability by extending interaction protocols using machine learning

Interaction protocols are commonly used in agent-based systems. They ensure good coordination between agents by proposing a specific message exchange pattern. However, these interaction protocols are not perfect; they need more extensions to offer, among others, better performance and scalability, mainly when tight deadlines are involved. In this case, participants often fail to answer some requests before their deadlines due to overload, bottlenecks, slow network, or being busy or blocked. Designing agents without considering this issue may decrease their sociability, which wastes valuable chances to obtain the best goals. The proposed approach uses the participant's experience to train supervised learning models to predict if the replies will reach initiators before deadlines or not, thereby enabling a prioritization mechanism for handling interaction requests more effectively. The proposed approach has been evaluated using multiple Contract Net interaction scenarios of two case studies under the JADE platform. The promising results show a significant increase in agents’ sociability measured by a new metric that we have proposed called Sociability Degree via Interaction Protocols (SD IP ) where it was maintained even when systems scale up in term of number of agents and initiated interactions.