expert system
In plain wordsA 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.
A computer program that solves problems in a narrow, specialized domain at a level approaching that of human experts by applying an explicit 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. — typically hundreds or thousands of if–then rules, frames or facts elicited from specialists — through 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. that chains rules forward from data or backward from hypotheses. Typical components include a working memory, an explanation facility that reports which rules fired, and a scheme for handling uncertainty such as certainty factors. Building one required knowledge engineering: interviewing experts and encoding their rules of thumb. Systems such as DENDRALDENDRAL, prop. n.: A program begun at Stanford in 1965 that worked out the structure of chemical molecules from laboratory measurements using rules taken from expert chemists. It is usually called the first expert system., 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. 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. made expert systems the first kind of AI to earn real money for its users, but they were brittle outside their domains, could not learn from data and were costly to maintain, and the collapse of the market in the late 1980s contributed to the second AI winterAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s..
The insurer's 1980s underwriting expert system had grown to six thousand rules that nobody fully understood.
Origin
Grew out of Edward Feigenbaum's Heuristic Programming Project at Stanford, where DENDRALDENDRAL, prop. n.: A program begun at Stanford in 1965 that worked out the structure of chemical molecules from laboratory measurements using rules taken from expert chemists. It is usually called the first expert system. (from 1965) showed that domain knowledge drove performance. The phrase's first use is not established, but it was current by the late 1970s; Feigenbaum's 1977 IJCAI lecture on knowledge engineering helped spread the approach, and by the mid-1980s corporations were spending over a billion dollars a year on AI, much of it on expert systems.
Usage note
Rule engines descended from expert systems remain in use, often unlabeled, in business-rule software, tax preparation and clinical decision support. The knowledge-engineering approach contrasts with 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., which acquires its knowledge from data.
Related
- 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.
- 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.
- rule-based systemrule-based system, n.: A computer system that makes decisions by following a list of 'if… then…' rules written by people, rather than by learning from examples.
- 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.
- DENDRALDENDRAL, prop. n.: A program begun at Stanford in 1965 that worked out the structure of chemical molecules from laboratory measurements using rules taken from expert chemists. It is usually called the first expert system.
- 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.
- AI winterAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s.
- Lisp machineLisp machine, n.: A 1970s–1980s personal workstation built specially to run the AI programming language Lisp quickly. When ordinary computers caught up, the market collapsed, helping to bring on an AI winter.
- 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
- Feigenbaum, E. A. (1977). The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering. Proceedings of IJCAI-77, 1014–1029.
Mentioned in
AI winterAI winter, n.: A slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s., explainable AIexplainable AI, n.: The field and set of methods that let people understand why an AI system produced a particular output — for example, which factors led it to reject a loan application., symbolic AIsymbolic AI, n.: An approach to AI that writes knowledge down as human-readable symbols and rules, such as 'every bird has wings', and reasons by manipulating them step by step instead of learning patterns from data., 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., Edward FeigenbaumEdward Feigenbaum, prop. n.: An American computer scientist who pioneered expert systems, programs that capture specialists' knowledge as rules to solve problems in fields such as chemistry and medicine., 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., good old-fashioned AIgood old-fashioned AI, n.: A nickname for the classic style of AI, dominant from the 1950s to the 1980s, that tried to make computers intelligent by writing knowledge and rules down by hand., IBM WatsonIBM Watson, prop. n.: An IBM question-answering computer that beat two champions of the US quiz show Jeopardy! in 2011. IBM then sold business AI products under the Watson name, many of which disappointed., inferenceinference, n.: Drawing a conclusion from evidence. In today's AI it most often means running an already-trained model to get an answer, as opposed to training it., 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., 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., Polanyi's paradoxPolanyi's paradox, n.: The observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples., PrologProlog, prop. n.: A programming language from 1972 in which you state facts and rules in logic and then ask questions; the computer finds answers by searching for proofs., rule-based systemrule-based system, n.: A computer system that makes decisions by following a list of 'if… then…' rules written by people, rather than by learning from examples., blackboard systemblackboard system, n.: A design for AI programs that lets many independent specialist modules collaborate only through a shared workspace: each watches it, posts a piece of the answer when it sees a chance, and builds on what others posted., case-based reasoningcase-based reasoning, n.: Handling a new situation by finding the most similar one dealt with before and adjusting what worked then, much as a cook adapts a remembered recipe to the ingredients at hand., cognitive computingcognitive computing, n.: A label, promoted heavily by IBM in the 2010s, for computer systems that learn, reason and interact with people in humanlike ways. It was largely a marketing term and has given way to 'AI'., 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., DENDRALDENDRAL, prop. n.: A program begun at Stanford in 1965 that worked out the structure of chemical molecules from laboratory measurements using rules taken from expert chemists. It is usually called the first expert system., Dreyfus's critique of AIDreyfus's critique of AI, n.: The philosopher Hubert Dreyfus's argument, from the 1960s on, that rule-based AI could never match human intelligence, because much human skill rests on bodily know-how and intuition rather than explicit rules., 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., knowledge acquisition bottleneckknowledge acquisition bottleneck, n.: The problem that getting knowledge out of human experts and into a computer by hand is so slow and costly that it limited how far classic expert systems could go., knowledge engineeringknowledge engineering, n.: The craft of collecting what human experts know — through interviews, observation and documents — and turning it into rules or structured data that a computer system can use.