Rule-based expert systems use programmed rules to solve problems, but require a large knowledge base and can provide inaccurate conclusions. They mimic human cognition and use if-then statements to narrow down causes and develop solutions. However, they can’t handle situations beyond their knowledge base and require clear communication with operators. Additional programming may be needed for language processing and artificial language skills.
Rule-based expert systems solve problems by applying a set of programmed rules to available information. These generally take the form of conditional sentences that the computer can use to logically check the data to reach a conclusion. Programming such systems requires a high level of skill and the incorporation of a large knowledge base. The conclusions reached by the system are not always accurate, although it can provide information on their statistical probability for the reference of technicians and operators.
In computer science, expert systems are designed to work like human experts to apply logic to problems. Instead of following strict programming rules, they are more flexible in nature and can mimic some paths of human cognition. The system can be used for tasks such as reviewing medical imaging studies, analyzing failures in a computer network, or identifying microorganisms. To work properly, it needs a logical basis 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. When 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 a power grid, in which case it would have a set of rules to determine the cause of a fault, then can recommend an action. These rule-based expert systems use logic that may be familiar to human experts using tree-like decision-making processes in evaluating problems.
This form of AI, however, isn’t perfect. Rule-based expert systems do not know how to handle situations that are beyond their knowledge and experience base. They can accumulate information over time, but the first instance of an abnormal event can create confusion for the system. It may return a false conclusion, requiring the operator to provide instructions so that it never makes the same mistake again. Sometimes a human would have been able to avoid the same mistake, illustrating the shortcomings of artificial cognition.
Logical interfaces in rule-based expert systems help them find answers, but they also need a method of communication. The data must be entered into the system for analysis and must have a way to interact with operators to provide an answer. This may require additional programming to help the system present information in simple, understandable language. If it returns garbled or unclear data, it is not helpful to the operator; some language processing and artificial language skills may therefore be required in programming and developing rule-based expert systems.
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