Advanced Bayesian Networks with Comparative Probability

The main purpose of this research is to enhance the current procedures of designing decision support systems (DSSs) used by decision-makers to comprehend the current situation better in cases where the available amount of information required to make an informed decision is limited. It has been suggested that the highest level of situation awareness can be achieved by a thorough grasp of particular key elements that, if put together, will synthesize the current status of an environment. The research resulted in a new innovated theory that combines the philosophical comparative approach to probability, the frequency interpretation of probability, dynamic Bayesian networks and the expected utility theory. It enables engineers to write self-learning algorithms that use example of behaviours to model situations, evaluate and make decisions, diagnose problems, and/or find the most probable consequences in real-time.