Expert systems

Expert Systems are computer programs that mimic the decision-making abilities of a human expert in a particular domain. They are a branch of Artificial intelligence (AI) and are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules.

The concept of expert systems dates back to the early 1960s. The first successful implementation was Dendral, a system for inferring possible molecular structures from mass spectrometry data.

Over the years, expert systems have evolved to include more sophisticated reasoning mechanisms, including fuzzy logic, neural networks, and genetic algorithms.

The knowledge base is the collection of facts and rules that the system uses to make decisions.

The inference engine is the core of the expert system, responsible for reasoning through the knowledge base to arrive at conclusions.

The user interface allows users to interact with the expert system, typically through a series of questions and answers.

These are the most common type of expert systems, which use a set of if-then rules to derive conclusions.

These systems use frames or slots, similar to object-oriented databases, to represent knowledge.

These systems combine various approaches, such as rule-based reasoning and neural networks, to improve performance and flexibility.

Expert systems are used in healthcare to assist doctors in diagnosing diseases based on symptoms.

In finance, expert systems are used for tasks such as credit scoring and risk assessment.

Expert systems can control manufacturing processes and detect faults in machinery.

While expert systems have proven useful in various domains, they also face challenges such as:

Knowledge Acquisition - Gathering and updating the knowledge base is a time-consuming process.
'Explainability - The reasoning process can be complex and difficult for humans to understand.