What Are Rule-Based Expert Systems?

Rule-based expert systems solve problems by applying a set of programmed rules to available information. These generally take the form of conditional sentences the computer can use to logically check data to reach a conclusion. Programming such systems requires a high level of skill and the incorporation of a big knowledge base. Conclusions reached by the system are not always accurate, although it can provide information about their statistical probability for the reference of technicians and operators.

In computing, expert systems are designed to work like human experts to apply logic to problems. Instead of following rigid programming rules, they are more flexible in nature, and can mimic some pathways of human cognition. The system can be used for activities like reviewing medical imaging studies, analyzing faults in a computer network, or identifying microorganisms. To function accurately, it needs a logical underpinning, and rules are a common choice.

The programmer uses the knowledge base to create a set of rules in the form of if-then statements. As rule-based expert systems encounter problems, they can apply these rules to narrow down the causes and develop solutions. For example, a system might monitor an electrical grid, in which case it would have a number of rules to determine the cause of a fault, so it can recommend an action. These rule-based expert systems use logic that can be familiar to human experts who use similar treed decision making in the evaluation of problems.

This form of artificial intelligence is not perfect, however. Rule-based expert systems don’t know how to handle situations that fall outside their knowledge base and experience. They can accumulate information over time, but the first instance of an abnormal event may be confusing for the system. It could return a false conclusion, which requires the operator to provide instruction so it won’t make the same mistake again. Sometimes a human would have been able to avoid the same error, illustrating the shortcomings in artificial cognition.

Logic interfaces in rule-based expert systems help them come up with answers, but they also need a communication method. Data needs to be fed into the system for analysis, and it must have a way to interact with operators to provide a response. This can require additional programming to help the system present information in plain, understandable language. If it returns gibberish or unclear data, it is not helpful to the operator; some language processing and artificial speech capacities may then be required in the programming and development of rule-based expert systems.