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Expert Systems

Definition

Expert systems are narrow rule-based programs that separate a knowledge base from an inference mechanism to reproduce parts of specialist decision-making.

What is an expert system?

An expert system is a computer program designed to reproduce part of the decision process of a human specialist in a narrow domain. Classic systems separate a knowledge base containing facts and rules from an inference engine that applies those rules.

Famous examples

DENDRAL, developed in the 1960s, helped infer molecular structures from mass-spectrometry data. MYCIN, developed in the 1970s, used rules to recommend antibiotics for certain bacterial infections. These systems showed that narrow expert knowledge could be encoded explicitly and applied consistently.

Why expert systems declined

Rule bases were expensive to build and maintain, and systems were brittle outside the knowledge that had been encoded. The commercial expert-system boom weakened in the late 1980s. Modern enterprise AI has revived some of the same goals, but usually combines statistical models, retrieval and deterministic business rules.

Related terms, defined

Classical AI & Machine Learning ELIZA and Early Conversational Programs ELIZA, created by Joseph Weizenbaum in the 1960s, used pattern matching and scripted text transformations to simulate conversation without modern statistical language understanding. Classical AI & Machine Learning SHRDLU and Language in a Blocks World SHRDLU, developed by Terry Winograd around 1968–1970, accepted natural-language instructions about a small simulated blocks world and combined language parsing with a symbolic world model. Classical AI & Machine Learning The AI Winters AI winters were periods in which enthusiasm and funding fell after systems failed to meet expectations or became economically unattractive. The term is especially associated with downturns in the 1970s and again in the late 1980s and early 1990s. Classical AI & Machine Learning Bayesian Networks A Bayesian network is a directed acyclic graph representing probabilistic relationships between variables using graph structure and conditional probability distributions. Classical AI & Machine Learning Fuzzy Logic Fuzzy logic allows degrees of membership between 0 and 1 rather than forcing every proposition into a strict true-or-false category.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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