AI comprises many varying technologies, each with its own specific goal. As its name implies, an expert system emulates tasks that would normally be performed by an expert in a particular field. Expert systems often implement a rule-based approach that uses boolean logic to process input from the user, employing stored rules to generate a prediction or suggestion. Breaking the Rules One of the major problems with rule-based approaches to expert systems is the fact that these approaches don't deal effectively with uncertainty. In the real world, however, the value of A variable is not always so crystal clear.
Uncertainty is the real challenge to artificial intelligence.
When we analyze a problem, after all, we often contend with uncertainty. To ignore this critical aspect of decision- making is to jeopardize the accuracy of any expert system. Oddly enough, Bayes was not famous for his mathematical ability. In fact, his contribution to probability theory, called Bayes' Rule, was published posthumously by a colleague. In the paper, Bayes' Rule illustrated a concept that would eventually become a cornerstone in the field of probability.