Expert system

Symbolics3640 Modified

BackwardChaining David C England 1990 p21

An expert system for the management of hazardous materials at a Naval Supply Center (IA anexpertsystemfo1094530640)

Expert system is a branch of artificial intelligence (AI) that makes use of specialized knowledge to solve problems at the level of a human expert. It is a computer system that emulates the decision-making ability of a human expert by reasoning through knowledge-based data. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. The primary components of expert systems are the knowledge base and the inference engine.

Knowledge Base[edit | edit source]

The knowledge base of an expert system contains both factual and heuristic knowledge. Factual knowledge is objective, verifiable, and widely accepted, while heuristic knowledge is subjective and experiential, often in the form of rules of thumb. This knowledge is typically curated and encoded into the system by human experts and knowledge engineers. The knowledge base is designed to be a comprehensive representation of the expertise in a specific domain, such as medicine, finance, or engineering.

Inference Engine[edit | edit source]

The inference engine is the component of an expert system that applies logical rules to the knowledge base to deduce new information or make decisions. It is the brain of the expert system, using various reasoning methodologies, such as forward chaining and backward chaining. Forward chaining starts with the available data and uses the inference rules to extract more data until a goal is reached. Backward chaining starts with a list of goals and works backward to determine what data must be true to achieve those goals.

User Interface[edit | edit source]

The user interface of an expert system allows for interaction between the user and the system. It is designed to be user-friendly, enabling users to input data, ask questions, and receive advice or recommendations. The sophistication of the user interface can vary significantly from simple text-based systems to complex graphical interfaces.

Applications[edit | edit source]

Expert systems are used in a wide range of applications, including medical diagnosis, financial analysis, manufacturing process control, sales forecasting, and customer service. In medicine, for example, expert systems can help diagnose diseases based on symptoms, medical history, and laboratory test results. In finance, they can analyze market data to make investment recommendations.

Advantages and Limitations[edit | edit source]

One of the main advantages of expert systems is their ability to store and manipulate significant amounts of knowledge. They can also work continuously, provide consistent answers, and reach areas where human experts are unavailable. However, expert systems have limitations, including the difficulty of acquiring and updating knowledge, the inability to learn from experience without human intervention, and the challenge of understanding complex human emotions and contexts.

Development Tools[edit | edit source]

Several tools and languages have been developed to create expert systems, including LISP, Prolog, and rule-based languages like CLIPS and Drools. These tools provide environments that facilitate the development of the knowledge base and inference engine.

Future Directions[edit | edit source]

The future of expert systems lies in integrating them with other AI technologies, such as machine learning and natural language processing, to enhance their capabilities. This integration allows expert systems to learn from new data, understand natural language inputs, and provide more accurate and context-aware decisions.

Expert system Resources
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