Advancing Blackboard Framework: Responsive Trigger Mechanisms and Dynamic Feasibility Conditions for Optimal Knowledge Source Selection

Expert systems (ES) rely heavily on effective control mechanisms to navigate complex problem-solving tasks. These mechanisms govern how ES utilizes knowledge and data to make decisions and execute actions. Optimizing these mechanisms, or addressing the “control problem,” remains a central challenge. Blackboard architectures, with their separation of knowledge and control processes, offer a promising approach. This study introduces an improved control mechanism for blackboard architectures. It focuses on a critical aspect-trigger mechanisms and feasible conditions-for optimal knowledge source selection to participate in the problem-solving process. By leveraging these features, this study aims to enhance decision-making and problem-solving capabilities within complex domains. This paper focuses on the technical details of exploring how it manages the flow of rule activation and execution throughout the problem-solving process.