Expert systems are artificial intelligence programs designed to mimic the decision-making abilities of human experts in specific domains. They consist of three main components: a knowledge base containing domain expertise, an inference engine that processes this knowledge, and a user interface for interaction. These systems capture expert knowledge and make it available for problem-solving and decision support.
Knowledge representation is crucial for expert systems to store and process information effectively. Rules use IF-THEN statements to encode expert logic. Frames organize knowledge into object-like structures with attributes and values. Semantic networks represent knowledge as graphs with nodes for concepts and links for relationships. Formal logic provides mathematical precision for complex reasoning. Each method has strengths for different types of knowledge and reasoning tasks.
An expert system shell is a software framework that provides the basic infrastructure needed to build expert systems. It includes a pre-built inference engine, user interface components, knowledge base structure, and development tools. The shell comes with an empty knowledge base that developers fill with domain-specific expertise. This approach significantly reduces development time and allows experts to focus on encoding knowledge rather than building the underlying system architecture.