knowledge representation
In plain wordsThe study of how to record knowledge about objects, categories, rules and events in a precise format, so that a program can reason with it and reach conclusions nobody typed in.
The subfield of AI that designs formal languages and data structures for encoding knowledge so that programs can draw conclusions from it, and that studies the trade-off between how much a language can express and how efficiently it can be reasoned with. Major formalisms include first-order logicfirst-order logic, n.: A precise formal language for statements about objects and their relations, using 'for all' and 'there exists', such as 'every person has a mother'. It is the standard logic of mathematics and of classic AI. and its decidable fragments such as description logicsdescription logic, n.: A family of precise logical languages for defining categories and how they relate, such as 'a parent is a person with at least one child', designed so that a computer's reasoning about them always finishes., rules and logic programslogic programming, n.: A style of programming in which you write down facts and rules in logic and then ask questions; the computer works out the answers itself by logical deduction., semantic networkssemantic network, n.: A way of storing knowledge as a web of concepts joined by labeled arrows, such as 'canary — is a — bird' and 'bird — can — fly', so a program can follow the links., framesframe, n.: In classic AI, a ready-made template for a familiar kind of thing or event, such as a kitchen or a job interview, with blanks to fill in and sensible default values., ontologiesontology, n.: A carefully defined vocabulary for a subject such as medicine, spelling out each kind of thing, how kinds nest inside one another, and which connections between them are allowed., action formalisms such as the situation calculussituation calculus, n.: A logical language for describing how the world changes as actions happen, so that a program can work out what will be true after any sequence of actions., and probabilistic representations such as Bayesian networksBayesian network, n.: A diagram of causes and effects with probabilities attached — say, how rain and a sprinkler can each make grass wet — that lets a computer update its beliefs about hidden causes when it sees evidence.. Usually paired with reasoning (KR&R), the field addresses defaults and exceptions (non-monotonic reasoningnon-monotonic reasoning, n.: Reasoning in which new information can overturn earlier conclusions — you assume a bird can fly until you learn it is a penguin — as people do constantly but classical logic does not allow.), time and change, uncertainty and common sensecommonsense reasoning, n.: The everyday background knowledge and reasoning people take for granted — dropped glasses break, hungry people eat, nobody is in two places at once — which machines long struggled to match.. In deep learningdeep learning, n.: Machine learning done with neural networks built from many stacked layers: raw data such as pixels, sound or text goes in at one end, and each layer turns what it receives into something more abstract., knowledge is instead stored implicitly in learned weights; representation learningrepresentation learning, n.: Letting a computer work out for itself how best to describe its data, such as the edges and shapes in images or the meanings of words, instead of relying on descriptions people design by hand. names that different enterprise.
Choosing a knowledge representation is choosing which inferences will be cheap and which impossible.
Origin
The phrase was in established AI use by the mid-1970s (it names the 1977 frame language KRL); its earliest use is not established. John McCarthy and Patrick Hayes's 1969 paper separated the 'epistemological' problem of representing the world from the 'heuristic' problem of searching for solutions, framing the field's agenda. The dedicated KR conference series began in Toronto in 1989.
Usage note
Davis, Shrobe and Szolovits (1993) argued that a representation plays five roles at once: a surrogate for the world, a set of ontological commitments, a fragmentary theory of intelligent reasoning, a medium for efficient computation and a medium of human expression.
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.
- ontologyontology, n.: A carefully defined vocabulary for a subject such as medicine, spelling out each kind of thing, how kinds nest inside one another, and which connections between them are allowed.
- first-order logicfirst-order logic, n.: A precise formal language for statements about objects and their relations, using 'for all' and 'there exists', such as 'every person has a mother'. It is the standard logic of mathematics and of classic AI.
- description logicdescription logic, n.: A family of precise logical languages for defining categories and how they relate, such as 'a parent is a person with at least one child', designed so that a computer's reasoning about them always finishes.
- semantic networksemantic network, n.: A way of storing knowledge as a web of concepts joined by labeled arrows, such as 'canary — is a — bird' and 'bird — can — fly', so a program can follow the links.
- frameframe, n.: In classic AI, a ready-made template for a familiar kind of thing or event, such as a kitchen or a job interview, with blanks to fill in and sensible default values.
- commonsense reasoningcommonsense reasoning, n.: The everyday background knowledge and reasoning people take for granted — dropped glasses break, hungry people eat, nobody is in two places at once — which machines long struggled to match.
- knowledge graphknowledge graph, n.: A huge web of facts stored as connections between things — 'Paris is the capital of France', 'Marie Curie won a Nobel Prize' — that computers can search, combine and reason over.
- representation learningrepresentation learning, n.: Letting a computer work out for itself how best to describe its data, such as the edges and shapes in images or the meanings of words, instead of relying on descriptions people design by hand.
Sources
- McCarthy, J. & Hayes, P. J. (1969). Some Philosophical Problems from the Standpoint of Artificial Intelligence. Machine Intelligence 4, 463–502.
- Davis, R., Shrobe, H. & Szolovits, P. (1993). What Is a Knowledge Representation? AI Magazine 14(1), 17–33.
- Brachman, R. J. & Levesque, H. J. (2004). Knowledge Representation and Reasoning. Morgan Kaufmann.
Mentioned in
artificial intelligenceartificial intelligence, n.: Computer systems that do things we would normally credit to a thinking mind, such as holding a conversation, reading an X-ray or planning a route, and the field of research that builds them., 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., commonsense reasoningcommonsense reasoning, n.: The everyday background knowledge and reasoning people take for granted — dropped glasses break, hungry people eat, nobody is in two places at once — which machines long struggled to match., first-order logicfirst-order logic, n.: A precise formal language for statements about objects and their relations, using 'for all' and 'there exists', such as 'every person has a mother'. It is the standard logic of mathematics and of classic AI., frameframe, n.: In classic AI, a ready-made template for a familiar kind of thing or event, such as a kitchen or a job interview, with blanks to fill in and sensible default values., John McCarthyJohn McCarthy, prop. n.: The American computer scientist who coined the term 'artificial intelligence', invented the Lisp programming language and championed formal logic as the route to machine common sense., 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., Marvin MinskyMarvin Minsky, prop. n.: An American AI pioneer who helped launch the field and MIT's AI laboratory, co-wrote a famous critique of early neural networks, and pictured the mind as a society of many simple parts., ontologyontology, n.: A carefully defined vocabulary for a subject such as medicine, spelling out each kind of thing, how kinds nest inside one another, and which connections between them are allowed., semantic networksemantic network, n.: A way of storing knowledge as a web of concepts joined by labeled arrows, such as 'canary — is a — bird' and 'bird — can — fly', so a program can follow the links., Association for the Advancement of Artificial IntelligenceAssociation for the Advancement of Artificial Intelligence, prop. n.: A major professional society for artificial intelligence researchers, founded in the United States in 1979, which runs a large yearly AI conference, publishes a magazine and honors leading scientists., conceptual dependencyconceptual dependency, n.: A 1970s theory for representing the meaning of sentences with a small set of basic actions, so that 'John gave Mary a book' and 'Mary got a book from John' come out the same., description logicdescription logic, n.: A family of precise logical languages for defining categories and how they relate, such as 'a parent is a person with at least one child', designed so that a computer's reasoning about them always finishes., IJCAIIJCAI, prop. n.: A worldwide conference spanning all branches of artificial intelligence, first held in 1969, before any of today's other leading AI meetings. It also presents some of the field's best-known awards., 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., MIT AI LabMIT AI Lab, prop. n.: The artificial intelligence laboratory at the Massachusetts Institute of Technology, founded in 1959 and one of the birthplaces of AI and hacker culture; it is now part of MIT's computer science lab, CSAIL., scriptscript, n.: In classic AI, a stored outline of a familiar sequence of events, such as eating at a restaurant, that helps a program understand a story by filling in what normally happens., situation calculussituation calculus, n.: A logical language for describing how the world changes as actions happen, so that a program can work out what will be true after any sequence of actions.