Self-Awareness Mechanism for Top-down Attention using Fuzzy Logic in Sustainable Business Intelligence

Purpose: The self-awareness mechanism can serve as inspiration for the design of an artificial intelligence system for top-down attention, for which self-awareness plays an indispensable role. When agents receive multiple stimuli from the environment, it becomes very difficult for them to focus solely on the most important stimulus. So, a self-awareness mechanism is required to regulate attention. Design/Methodology/Approach: This paper proposes the concept of a self-awareness mechanism utilizing fuzzy logic to modulate the selection of high-priority stimuli within a priority-based system. Utilizing a prioritization technique and fuzzy logic to identify the most important stimulus, this mechanism enhances the agent's self-awareness and self-control mechanisms. Findings: The results reveal that the self-awareness mechanism renders cognitive functions present in the human mind: expert systems can manage human legible knowledge and make inference upon it, such formulation allows to build a system that manages imprecise information, an artificial neural network-based cognitive structure that can learn, generalize, and prioritize all complications. Implications/Originality/Value: The study posits that fuzzy logic rules can be defined according to the priority of the input environment stimuli to generate a fuzzy output in the form of the most important stimulus.