<![CDATA[This research aims to develop a unified mathematical formulation to optimize expert systems by integrating advanced techniques in knowledge representation, inference mechanisms, machine learning, and parallel/distributed processing. The primary objective is to enhance decision-making efficiency in expert systems by optimizing the interaction between these components. The research design focuses on building a comprehensive model that combines ontology-based and frame-based knowledge representation, forward and backward chaining inference, neural networks, Bayesian networks, fuzzy logic, and parallel computing. The methodology includes defining efficiency metrics for each component and combining them into a single optimization model. A numerical example was tested using simulated data to evaluate the performance of the proposed system. Key results show that frame-based knowledge representation, forward chaining, and parallel processing contribute significantly to overall system efficiency. The neural network's low loss function and the Bayesian network's high likelihood value confirm the effective integration of machine learning into the expert system. The research concludes that the unified optimization framework significantly improves decision-making efficiency, with a total efficiency score of 23.09. This approach fills a gap in previous studies, which often focus on individual components in isolation, by providing a holistic model that optimizes all aspects of expert systems simultaneously. Future research should focus on real-world implementations and fine-tuning the model to handle dynamic environments and complex decision-making tasks.]]>
