Expert systems use rules to make decisions in specific domains from "summary" of Artificial Intelligence by Melanie Mitchell
Expert systems are a type of AI application that rely on rules to make decisions within a specific domain. These rules are typically formulated by human experts in the field, who encode their knowledge and decision-making processes into the system. The goal is to mimic the problem-solving abilities of human experts in that domain, allowing the system to make decisions or recommendations based on the rules provided.
In essence, expert systems operate by reasoning through a set of rules to arrive at a conclusion or decision. When presented with a new problem or question, the system consults its knowledge base and applies the rules that are relevant to the situation. By following this process, the system can provide solutions, diagnoses, or recommendations that are consistent with the expertise of human professionals in the domain.
One of the key advantages of expert systems is their ability to capture and leverage expert knowledge in a structured and reproducible manner. This knowledge can be difficult to articulate or teach directly, but by encoding it into rules, it becomes accessible to the system. As a result, expert systems can perform complex decision-making tasks with a level of expertise and consistency that is difficult to achieve through traditional software programs.
However, it is important to note that expert systems are limited by the rules that are provided to them. If a situation arises that falls outside the scope of the rules or if the rules are incomplete or incorrect, the system may struggle to provide a meaningful response. This is known as the "knowledge acquisition bottleneck," where acquiring, validating, and updating the rules can be a challenging and time-consuming process.
Despite these limitations, expert systems have been successfully applied in a wide range of domains, including medicine, finance, engineering, and more. By leveraging the expertise of human professionals and translating it into rules, these systems can provide valuable decision support and problem-solving capabilities in specialized areas of knowledge.
