Probabilistic Reasoning Using the Normal Distribution for Traffic Light Controller

Recently, researchers have used different types of uncertainties to describe uncertain information. These uncertainties may be related to Experimental Errors, Temperature variation, Climate change, Equipment faults, traffic problems, and so on. When choosing an information processing tool, the study of the nature of information uncertainty is noteworthy. In this regard, fuzzy logic and its combinations are preferred in new scientific literature. Part of the logical reasoning process based on Zade’s number theory is probabilistic reasoning (PR). Recently, the probabilistic approach has delivered a well-structured framework for probabilistic knowledge representation, modeling, and analysis of random events. On top of that, the PR method doesn’t differentiate between incomplete information, mistakes that cause doubt, and uncertainty itself. However, PR expresses the shortcomings of incomplete knowledge, computational complexity, and limitation axioms of probability theory, and the advantage of good understanding. Fuzzy logic and probabilistic reasoning is the basic combination of the components of Soft computing. When it comes to representing information, fuzzy logic theory is an obvious choice. However, processing the data expressed by their combination is a complex issue and there are scarce studies in this field. In this article, the problem of probabilistic reasoning is considered in the example of a traffic light controller.