Expert Systems
What is Expert Systems?
Expert Systems are a significant part of the Artificial Intelligence industry, designed to mimic the decision-making abilities of human experts. These systems employ a vast amount of specialized knowledge and rule-based logic to solve complex problems that typically require human intelligence. They are built on a knowledge base and an inference engine, where the former contains domain-specific information and the latter applies logical rules to the knowledge base to infer new information or make decisions. Unlike traditional software, which follows straightforward programmed instructions, expert systems can reason through bodies of knowledge, making them invaluable in fields like medicine, finance, and customer service. These systems can provide explanations and recommendations, thus aiding human decision-makers by offering expert-level insights and solutions.
A branch of Artificial Intelligence that uses knowledge-based systems to simulate the decision-making ability of a human expert.
Examples
- MYCIN: Developed in the 1970s at Stanford University, MYCIN was designed to diagnose bacterial infections and recommend antibiotics. It was one of the earliest examples of an expert system used in medical diagnosis and it demonstrated the potential of AI in healthcare.
- DENDRAL: Another early expert system, DENDRAL was created to analyze molecular structures. It helped chemists by identifying unknown organic molecules based on their mass spectrometric data, showing how AI can assist in scientific research.
Additional Information
- Expert systems are typically implemented using languages like Prolog or Lisp.
- They are often used in situations where the expertise is rare or expensive to come by.