An expert system is an artificial intelligence program designed to solve problems and make decisions in a specific area of knowledge by using the knowledge and reasoning methods of human experts. It attempts to reproduce the decision-making ability of a specialist and provides solutions, recommendations, diagnoses, classifications, or explanations for problems that normally require human expertise. Expert systems are particularly useful when the problem domain is well defined and the knowledge required to solve the problem can be represented in a structured form. An expert system generally consists of a collection of expert knowledge and a reasoning mechanism that applies this knowledge to a particular problem. For example, an expert system in medical diagnosis can use symptoms, patient information, and medical rules to identify possible diseases. In a computer troubleshooting system, it can use information about errors and system conditions to identify the likely cause of a problem and recommend corrective actions. In the context of a Programming and AI Lab, an expert system demonstrates important artificial intelligence concepts such as knowledge representation, inference, rule-based reasoning, decision-making, and explanation. A generic expert system shell is a software framework that provides the basic mechanisms required to build an expert system without containing the complete domain-specific knowledge itself. The shell separates the general problem-solving mechanism from the knowledge of a particular application. Thus, the same shell can be used to develop different expert systems by changing or adding the appropriate knowledge base. For example, a shell could be used to create a medical diagnosis system by adding medical rules or a computer troubleshooting system by adding troubleshooting rules. The major component of a generic expert system shell is the knowledge base. The knowledge base stores the specialized knowledge required to solve problems in a particular domain. This knowledge may include facts, rules, concepts, relationships, procedures, and heuristics obtained from human experts, textbooks, databases, or other reliable sources. In a rule-based expert system, knowledge is commonly represented using IF-THEN rules. For example, a rule may be represented as: IF a computer does not start AND the power indicator is off, THEN check the power supply. Such rules provide the expert system with domain-specific knowledge. Facts represent information that is known to be true about a particular problem or situation. For example, in a diagnostic system, facts may include “temperature is high,” “cough is present,” or “battery voltage is low.” Rules use these facts to derive new information. The knowledge base therefore provides the foundation on which the expert system performs reasoning. Another important component is the inference engine. The inference engine is the reasoning mechanism of the expert system. It examines the facts available in working memory and applies appropriate rules from the knowledge base to derive conclusions. It determines which rules are applicable and controls the sequence in which they are executed. Two common reasoning methods used by inference engines are forward chaining and backward chaining. Forward chaining is data-driven reasoning. It starts with known facts and repeatedly applies rules to derive new facts until a conclusion is reached. For example, if the system knows that a machine is not receiving power and has a rule stating that a machine without power may have a faulty power supply, it can derive the possibility of a power-supply problem. Backward chaining is goal-driven reasoning. It starts with a possible conclusion or goal and works backward to determine whether the available facts and rules support that conclusion. For example, if the goal is to determine whether a computer has a faulty power