How does KEEL differ from conventional AI Expert Systems?
Conventional rule-based expert systems use approaches like Forward and Reverse Chaining. Reverse chaining systems start with a solution and work back through all the data to determine whether the solution was valid. This approach has worked for simple decisions when some data might be missing. Forward chaining systems start with the data and try to determine the solution, but suffer from missing information components. Rule-based systems supplied the concepts of confidence factors or certainty factors as part of the math behind the results. These types of systems were commonly used to evaluate static problems where the rules are fixed and the impact of each rule is stable. In many real world decision-making situations rule based systems quickly become complex and hard to understand. Computer programs based on rule based systems are usually expensive to develop and difficult to debug. Furthermore, they can be inflexible, and if changes occur, may require complete recoding of system solu
