Expert Systems

What are Expert Systems?

Expert systems are a form of artificial intelligence from the 1980s that were designed to mimic the decision-making abilities of a human expert in a specific, narrow field. Unlike modern generative AI, which learns broad patterns from massive datasets, expert systems were knowledge-based, meaning they were programmed with a rigid set of facts and rules provided by human specialists.

How They Worked and Were Built

Expert systems had two main components:

  • A Knowledge Base: This was the “brain” of the system, containing all the codified facts and heuristics (rules of thumb) for a particular domain. This knowledge was typically gathered through a labor-intensive process called knowledge engineering, where a human “knowledge engineer” would interview and extract information from domain experts.
  • An Inference Engine: This was the “logic” part of the system. It would use reasoning strategies like forward chaining (starting with data to reach a conclusion) or backward chaining (starting with a goal and working backward to find supporting facts) to apply the rules in the knowledge base to a user’s query and arrive at a solution.

A key part of their development was the use of specialized programming languages like LISP and, later, “expert system shells” that provided the framework for building a new system by simply adding a new knowledge base.

Major Expert Systems

  • XCON: Mentioned in the Canvas, XCON (for eXpert CONfigurer) was a highly successful system developed for Digital Equipment Corporation. It was used to configure computer orders and saved the company millions of dollars by ensuring all components were compatible.
  • MYCIN: Developed at Stanford in the 1970s, MYCIN was a system designed to diagnose bacterial infections and recommend antibiotics. It was notable for being able to perform at a level comparable to human experts in its narrow field.
  • DENDRAL: Another Stanford project, this system assisted organic chemists in identifying unknown molecular structures.

Limitations and Current Status

Expert systems faced several critical limitations that ultimately led to the second “AI winter”:

  • Brittleness: They lacked common sense and could not handle situations outside of their predefined rules. If they encountered a problem not in their knowledge base, they would fail completely.
  • Maintenance Nightmare: The knowledge bases were difficult and expensive to maintain. As expert knowledge in a field evolved, the rules had to be manually updated, a time-consuming process.
  • Scalability: The rule-based approach did not scale well to complex, large-scale problems.

Today, pure expert systems are largely considered obsolete. However, their core concepts—separating a knowledge base from a reasoning engine—are still present in modern AI. Components of expert systems have been integrated into hybrid systems, often working in tandem with machine learning models. For example, a modern AI system might use a deep learning model for pattern recognition but still use a rule-based component for critical, logical decisions.