rule-based system
In plain wordsA computer system that makes decisions by following a list of 'if… then…' rules written by people, rather than by learning from examples.
A system whose behavior is determined by an explicit set of human-authored rules, typically of the form 'if conditions, then conclusion or action', applied to facts by an inference engineinference engine, n.: The part of an AI system that does the working-out: in older AI, the program that applies rules to facts; in today's AI, the software that runs a trained model to produce answers. using forwardforward chaining, n.: A way of reasoning that starts from known facts and keeps applying rules to them, adding each new conclusion, until it reaches an answer or nothing new can be concluded. or backward chainingbackward chaining, n.: A way of reasoning that starts from the question ('is this true?') and works backward, looking for rules that would prove it and then checking whether their conditions hold.. The rules form a knowledge baseknowledge base, n.: A store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up. that domain experts can read and audit, which is why rule-based systems remain common where decisions must be explained or certified: credit and insurance underwriting, fraud screening, clinical alerts, benefit eligibility, network configuration. Handling uncertainty required additions such as the certainty factors of MYCINMYCIN, prop. n.: A 1970s Stanford program that advised doctors on blood infections and antibiotics by applying hundreds of if–then rules from specialists. It performed about as well as experts but was never used on patients. or fuzzy logicfuzzy logic, n.: A kind of logic that allows degrees of truth, so a statement like 'the room is warm' can be 70 percent true, which helps machines handle vague everyday categories.. Weaknesses are brittleness when inputs fall outside the rules, unexpected interactions among thousands of rules, and the effort of eliciting and maintaining them. Many deployed AI products wrap learned models in rule layers for policy enforcement and guardrailsguardrails, n.: Safety checks built around an AI system — filters on what goes in and comes out, and limits on what it may do — to keep it from producing harmful or off-topic content or leaking data..
Compliance insisted the final approval step stay rule-based so every decision can be traced to a written policy.
Origin
Rule-based programs grew out of production systemsproduction system, n.: A program built from many 'if this, then do that' rules plus a memory of current facts; it repeatedly finds a rule whose 'if' part matches and carries out its 'then' part.. MYCIN (Stanford, 1972–1976) made production rules the standard vehicle for encoding expert knowledge, and XCONXCON, prop. n.: A rule-based program used from 1980 by the computer maker Digital Equipment Corporation to work out which parts each customer's computer order needed. It was the first big commercial success of expert systems., written in OPS5 and used at Digital Equipment Corporation from 1980 to configure computer orders, demonstrated their commercial value.
Usage note
Rule-based is often contrasted with learning-based, but the two blend: decision treesdecision tree, n.: A model that makes a prediction by asking a series of yes-or-no questions about the input, like a flowchart, with each answer leading to the next question until it reaches a final answer. and association rule learningassociation rule learning, n.: Finding 'if this, then often that' patterns in large records of transactions, such as noticing that shoppers who buy pasta and tomatoes often buy parmesan too. learn rules from data, and inductive logic programminginductive logic programming, n.: A kind of machine learning that discovers general logical rules — such as 'X is a grandparent of Z if X is a parent of a parent of Z' — from examples and background knowledge. learns logical rules.
Related
- expert systemexpert system, n.: A program that stores a human specialist's know-how as many if–then rules and uses them to give advice or make decisions in a narrow field, such as diagnosing infections or configuring computers.
- production systemproduction system, n.: A program built from many 'if this, then do that' rules plus a memory of current facts; it repeatedly finds a rule whose 'if' part matches and carries out its 'then' part.
- inference engineinference engine, n.: The part of an AI system that does the working-out: in older AI, the program that applies rules to facts; in today's AI, the software that runs a trained model to produce answers.
- knowledge baseknowledge base, n.: A store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up.
- forward chainingforward chaining, n.: A way of reasoning that starts from known facts and keeps applying rules to them, adding each new conclusion, until it reaches an answer or nothing new can be concluded.
- backward chainingbackward chaining, n.: A way of reasoning that starts from the question ('is this true?') and works backward, looking for rules that would prove it and then checking whether their conditions hold.
- fuzzy logicfuzzy logic, n.: A kind of logic that allows degrees of truth, so a statement like 'the room is warm' can be 70 percent true, which helps machines handle vague everyday categories.
- machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats.
Sources
- Buchanan, B. G. & Shortliffe, E. H. (eds.) (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley.
Mentioned in
machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats., AI-poweredAI-powered, adj.: Described as using artificial intelligence to work. The label appears on everything from search engines to toothbrushes and often says little about how much AI is really involved., expert systemexpert system, n.: A program that stores a human specialist's know-how as many if–then rules and uses them to give advice or make decisions in a narrow field, such as diagnosing infections or configuring computers., forward chainingforward chaining, n.: A way of reasoning that starts from known facts and keeps applying rules to them, adding each new conclusion, until it reaches an answer or nothing new can be concluded., fuzzy logicfuzzy logic, n.: A kind of logic that allows degrees of truth, so a statement like 'the room is warm' can be 70 percent true, which helps machines handle vague everyday categories., inference engineinference engine, n.: The part of an AI system that does the working-out: in older AI, the program that applies rules to facts; in today's AI, the software that runs a trained model to produce answers., MYCINMYCIN, prop. n.: A 1970s Stanford program that advised doctors on blood infections and antibiotics by applying hundreds of if–then rules from specialists. It performed about as well as experts but was never used on patients., Dempster–Shafer theoryDempster–Shafer theory, n.: A theory of reasoning with uncertain evidence that lets you hold back belief — in effect saying 'I don't know' — instead of being forced to divide all your confidence among the possibilities., Harold CohenHarold Cohen, prop. n.: A British-born painter who wrote AARON, a program that made its own drawings and paintings from the early 1970s, making him a pioneer of art produced by AI., inductive logic programminginductive logic programming, n.: A kind of machine learning that discovers general logical rules — such as 'X is a grandparent of Z if X is a parent of a parent of Z' — from examples and background knowledge., Lotfi ZadehLotfi Zadeh, prop. n.: An Azerbaijani-born American engineer who invented fuzzy logic, which lets computers reason with vague categories such as 'warm' or 'tall' that are true only to a degree., production systemproduction system, n.: A program built from many 'if this, then do that' rules plus a memory of current facts; it repeatedly finds a rule whose 'if' part matches and carries out its 'then' part., XCONXCON, prop. n.: A rule-based program used from 1980 by the computer maker Digital Equipment Corporation to work out which parts each customer's computer order needed. It was the first big commercial success of expert systems.