Expert System
An expert system encodes a human expert's domain knowledge as hand-written if-then rules plus an inference engine, the dominant AI approach of the 1970s-80s.
An expert system encodes a human expert's domain knowledge as a large
base of hand-written if-then rules, plus an inference engine to chain
them together — the dominant AIAI (Artificial Intelligence)AI is the field of building systems that perform tasks normally requiring human intelligence — reasoning, perception, language, and decision-making. approach of the
1970s–80s (e.g. MYCIN for medical diagnosis). Expert systems were
genuinely useful for narrow domains but didn't generalize or scale: every
new domain needed a new expert to interview and a new rule base to
maintain by hand — the bottleneck that
machine learningMachine Learning (ML)Machine learning is the practice of writing programs that learn a function from data, via a loss function and an optimizer, instead of following hand-written rules. later solved by learning
patterns from data instead of encoding them manually.
How it works
An expert system separates two components. The knowledge base holds
declarative rules — IF fever AND stiff-neck THEN suspect meningitis —
and the inference engine applies them. Chaining runs in one of two
directions: forward chaining starts from known facts and derives
whatever conclusions they support, while backward chaining starts
from a hypothesis and searches for rules whose conclusions would
establish it, recursively asking the user for facts it's missing. Rules
often carry confidence weights so competing conclusions can be ranked,
and a conflict-resolution strategy decides which of several matching
rules fires first. Because every step is an explicit rule, the system
can print the chain that produced an answer — an explainability
property deep learningDeep LearningDeep learning is machine learning using multi-layer neural networks, which learn their own features from raw data instead of relying on hand-engineered ones. models lack.
When it breaks
- Knowledge acquisition is the bottleneck. Every rule has to be extracted by interviewing an expert, and experts are poor at articulating judgment they apply unconsciously.
- Rule bases turn brittle past a few hundred rules. New rules interact with old ones in ways nobody anticipated, and no maintainer can hold the full interaction surface in their head.
- Coverage is exactly the enumerated cases. Anything outside them yields no answer rather than a degraded one.
- The approach survives where inputs are structured and decisions must be auditable — tax and compliance engines, underwriting, clinical decision support — and increasingly as guardrails constraining LLMLLM (Large Language Model)An LLM is a large transformer trained to predict the next token on massive text corpora, then fine-tuned to follow instructions — the architecture behind GPT, Claude, Gemini, and Llama. output rather than as a replacement for it.
See also: AIAI (Artificial Intelligence)AI is the field of building systems that perform tasks normally requiring human intelligence — reasoning, perception, language, and decision-making., Machine LearningMachine Learning (ML)Machine learning is the practice of writing programs that learn a function from data, via a loss function and an optimizer, instead of following hand-written rules.
Learn more: History & Landscape · Wikipedia: Expert system
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