Also known as artificial intelligence, knowledge-based systems are so named because they accumulate knowledge and, with it, the ability to make decisions or predict the future based on knowledge of historical data.
Two well-known types of knowledge-based systems are expert systems and neural networks. We explain each in the following sections.
Expert systems build a database of past events in order to predict outcomes in future situations. An inference engine analyzes the past events to see whether it can find a match between a past event and the current problem. For instance, if a stock-picking program knows that IBM always goes up two points when the Mets are in town under a full moon, then it tells you to buy IBM when the Mets are in town and the moon is full.
Expert systems are designed to work with degrees of uncertainty, and they do so in one of two ways:
Fuzzy logic: Breaks down the factors influencing a decision or outcome into its components, evaluates each individual component, and then recombines the individual evaluations in order to arrive at the yes/no or true/false conclusion for the big question or problem.
Certainty factors: Operate on the numeric probability of yes/no, true/false, rain/snow, or whatever the expert system is ...