This document describes a computational decision making system for fault diagnosis under a cognitive theory framework. It proposes assigning prior probabilities to the system based on expert opinion and reliability of the primary decision making system. The system adopts a probability distribution as its beliefs and updates beliefs using conditionalization to generate posterior probabilities. It evaluates decisions based on expected epistemic utility to maximize acquiring error-free knowledge. Applying this methodology, it assigns prior probabilities to the system for various normal and fault conditions. It reassesses beliefs based on fault diagnosis results to improve functioning.