Symbolic & Classical AI

Logic, search, planning and knowledge representation: AI before deep learning, and alongside it. 111 entries live here, and 93 from other domains touch it.

Essential

symbolic AIAn 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.

All other entries

110

A* searchA method for finding the shortest route on a map or network that weighs both how far you have already come and a smart guess of how far is left, so it tries the most promising paths first.
abductive reasoningReasoning from an observation to its most likely explanation — the grass is wet, so it probably rained — the kind of educated guess that doctors and detectives make.
admissible heuristicAn estimate of the remaining distance to a goal that is never more than the real distance — an optimistic guess — which lets some search methods guarantee that they find the best route.
AI-completeDescribes a problem believed to be so hard that solving it would require general, human-level intelligence, such as flawless translation or truly understanding a story.
alpha–beta pruningA shortcut for game programs: tracking the best result each side is already sure of, it ignores lines of play that could only do worse, and when promising moves come first it searches about twice as deep.
answer set programmingA way to solve hard puzzles and scheduling problems by describing in logical rules what a valid solution looks like; a solver then finds the arrangements that fit those rules.
ant colony optimizationA method for finding good routes and schedules inspired by how ants find short paths to food: simulated ants leave 'scent' on good paths, which attracts other ants, so the best routes gradually stand out.
autoformalizationUsing AI to translate ordinary mathematics written for people — definitions, theorem statements, proofs — into a strict formal language that a proof-checking program can verify.
automated planningThe branch of AI that works out a sequence of actions to reach a goal, such as the steps a robot must take to assemble a part, from descriptions of what each action needs and what it changes.
automated theorem provingGetting computers to prove mathematical or logical statements by themselves, by searching for a chain of valid steps that leads from basic assumptions to the statement.
backward chainingA 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.
Bayesian networkA 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.
belief propagationA way for a network of uncertain quantities to reach conclusions by having each node pass short messages to its neighbors about what it currently believes, until the beliefs settle down.
belief–desire–intention modelA way of designing software agents around three human-like ideas: what the agent believes about the world, what it wants, and which plans it has committed to carrying out.
best-first searchA family of search methods that rank every option waiting to be explored by a score, such as an estimate of the distance still to go, and always take the top-ranked one next.
blackboard systemA 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.
blocks worldA simplified pretend world of toy blocks on a table that early AI researchers used to test programs for planning, seeing and understanding language, because it was simple enough to describe completely.
Boolean satisfiability problemThe puzzle of deciding whether a set of true-or-false conditions can all be met at once. It is famously hard in theory, yet modern solvers routinely handle millions of conditions.
branch and boundA method for finding the provably best answer to a hard puzzle: divide the possibilities into groups, quickly estimate the best any group could offer, and throw out every group that cannot beat the best answer already in hand.
breadth-first searchA way of exploring a maze or network level by level — first every place one step away, then every place two steps away, and so on — so that the shortest route is found first.
case-based reasoningHandling 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.
closed-world assumptionThe rule of treating whatever a system cannot confirm as false: if a library's catalog does not list a book, the library is assumed not to have it.
cognitive architectureA blueprint for a complete thinking system — how memory, attention, learning and decision-making fit together — used both to build general AI agents and to model how the human mind works.
combinatorial explosionThe way the number of possible combinations — moves, routes, schedules — grows so quickly as a problem gets bigger that checking them all becomes impossible, even for the fastest computers.
commonsense reasoningThe 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.
ConceptNetA free, crowd-built network of everyday facts — 'a knife is used for cutting', 'rain makes things wet' — in many languages, which AI systems use to make sense of ordinary situations.
conceptual dependencyA 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.
constraint satisfaction problemA problem in which each item must be given a value so that every rule is obeyed, like filling in a Sudoku, coloring a map so no neighbors match, or building a timetable without clashes.
CycAn AI system built by specialists who spent decades typing in millions of everyday truths, such as 'people cannot be in two places at once', hoping to give computers common sense.
DatalogA small logic language for asking questions of a database with rules, including rules that refer to themselves, such as 'X is an ancestor of Y if X is a parent of an ancestor of Y'.
deductive reasoningReasoning in which true starting points guarantee a true conclusion: if every square is a rectangle and this shape is a square, then it must be a rectangle.
Dempster–Shafer theoryA 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.
depth-first searchA way of exploring a maze or network by following one path as far as it goes, then backing up to the most recent fork and trying the next branch.
description logicA 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.
Dijkstra's algorithmA classic method for finding the shortest routes from one starting point to every other point on a map or network, by always settling the nearest not-yet-settled point next.
evaluation functionIn a game-playing program, a formula that looks at a board position and gives it a score saying how good it is, without playing the game out to the end.
evolution strategiesA family of evolution-inspired methods for tuning a list of numbers: try many random variations of the current best settings, keep those that work better, and adjust how big the variations are as you go.
evolutionary computationProblem-solving by simulated evolution, much as breeders improve crops: keep many candidate answers, let the better ones produce slightly altered offspring, drop the worse, and repeat for many rounds.
first-order logicA 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.
fitness functionIn evolution-inspired computing, the scoring rule that says how good each candidate solution is, which decides which candidates survive and get to 'reproduce'.
formal verificationUsing mathematics, not just testing, to prove that a computer system always behaves as specified — for example, that a chip never adds wrongly or that a program can never crash in a particular way.
forward chainingA 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.
frameIn 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.
FunSearchA Google DeepMind method that discovered new mathematical constructions by having a language model repeatedly rewrite short computer programs, keeping the versions that scored best.
fuzzy logicA 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.
game treeA branching diagram of every way a game could unfold: each point is a position, each branch a possible move, and the tips of the branches are wins, losses or draws.
general game playingBuilding AI players that are handed the rules of a game they have never seen and must work out how to play it well on their own, with no game-specific help from their programmers.
genetic algorithmA problem-solving method that copies natural evolution: many candidate solutions compete, the best are 'bred' by mixing and randomly tweaking them, and over many generations the solutions improve.
genetic programmingUsing simulated evolution to write computer programs: random programs compete at a task, and the best are combined and mutated over many generations until one does the task well.
good old-fashioned AIA 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.
heuristicA rule of thumb: a quick, usually good-enough way to make a decision or to estimate how close you are to a goal, without any guarantee of finding the best answer.
hidden Markov modelA model of a sequence, such as speech or DNA, in which hidden states — say, the words being spoken — change step by step at random, and each state gives off signals that can be observed, such as sounds.
hierarchical task networkA planning method that breaks a big task, such as 'travel to Paris', into smaller tasks, such as 'book a flight' and 'pack', using stored recipes, until only simple actions remain.
hill climbingA simple search method that keeps making whichever small change improves things, like climbing in fog by always stepping uphill — which can leave it stuck on a small hill instead of the highest peak.
Horn clauseA simple, restricted form of logical rule ('if these conditions all hold, then this one conclusion holds') that computers can reason with very efficiently.
inductive logic programmingA 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.
inductive reasoningReasoning from particular examples to a general rule or prediction — every swan I have seen is white, so swans are probably white — which is useful but can always turn out wrong.
inference engineThe 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.
interactive theorem provingWriting a proof in a precise computer language so that software checks every step: the person supplies the ideas, the computer fills in small steps and guarantees that nothing is wrong.
IsabelleA widely used proof-checking program in which people write mathematics and verified software; it is known for automation that calls other theorem provers to finish routine steps.
iterative deepening searchA search strategy that explores one step deep, then starts over and explores two steps deep, then three, and so on. It repeats some work but needs very little memory and still finds the shortest answer.
knowledge acquisition bottleneckThe 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 baseA 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.
knowledge engineeringThe 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.
knowledge graphA 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.
knowledge graph embeddingA way of turning the things and relations in a knowledge graph into lists of numbers, so a computer can guess missing facts from patterns, such as predicting where someone was born.
knowledge representationThe 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.
LeanA programming language and proof checker in which mathematicians write proofs that the computer verifies line by line. It has become the main language in which AI systems produce checkable proofs.
local searchA family of problem-solving methods that start with some complete answer, however poor, and keep improving it by small changes, instead of building an answer piece by piece from scratch.
logic programmingA 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.
Markov logic networkA way of combining logical rules with probabilities, so that a rule like 'friends usually like the same music' can sometimes be broken, with each rule given a weight saying how strongly it holds.
Markov random fieldA model of many linked uncertain quantities, such as neighboring pixels in a photo, in which each one depends directly only on its neighbors, with no notion of which one causes which.
MathlibA huge shared library of mathematics written in the Lean language, in which every definition and proof has been checked by computer. Mathematicians keep extending it, and AI proof systems are trained on it.
means–ends analysisA problem-solving method that compares where you are with where you want to be, picks an action that shrinks the biggest difference, and sets itself smaller goals when that action cannot yet be taken.
metaheuristicA general-purpose strategy for finding good answers to hard problems, such as imitating evolution or cooling metal, that can be adapted to many different problems instead of being designed for just one.
microworldA small, deliberately simplified artificial setting, such as a table of toy blocks, in which an AI program can be tested in full detail before anyone tries it in the messy real world.
minimaxA way for a game-playing program to choose moves by assuming the opponent will always make their best reply, and picking the move whose worst-case outcome is best.
neuroevolutionTraining or designing neural networks by simulated evolution — breeding and mutating whole networks and keeping the best performers — instead of, or alongside, the usual gradient-based training.
neuro-symbolic AIAI that combines neural networks, which learn patterns from data, with symbolic methods, which follow explicit rules and logic, aiming for systems that both learn flexibly and reason reliably.
non-monotonic reasoningReasoning 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.
ontologyA 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.
particle swarm optimizationAn optimization method inspired by flocking birds: candidate solutions 'fly' through the space of possibilities, each pulled toward the best spot it has found and the best spot its neighbors have found.
physical symbol system hypothesisThe 1976 claim by two AI pioneers that a machine which stores and rearranges symbols according to rules has everything needed for general intelligence, and that anything generally intelligent must be such a machine.
Planning Domain Definition LanguageA standard computer language for describing planning problems — the available actions, the starting situation and the goal — so that any automated planner can read and solve them.
probabilistic graphical modelA way of describing how many uncertain quantities depend on one another by drawing them as a network, which makes it practical for a computer to reason about all of them together.
probabilistic programmingWriting a statistical model as an ordinary-looking program that includes random choices, and letting the system work out automatically which hidden values best explain the observed data.
production systemA 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.
program synthesisGetting a computer to write a program automatically from a description of what the program should do — a logical specification, a few input–output examples or a request in plain language.
propositional logicThe simplest formal logic: whole statements that are either true or false, joined by 'and', 'or', 'not' and 'if… then', with exact rules for working out whether the combination is true.
quality diversityA family of evolution-inspired search methods that aim to collect many different good solutions — for example, many distinct ways for a robot to walk — rather than a single best one.
resolutionA mechanical step of logical reasoning: from 'A or B' and 'not A or C', conclude 'B or C'. Repeating it can demonstrate any conclusion that genuinely follows from what is known.
Resource Description FrameworkA web standard for writing facts as simple three-part statements — subject, relation, object — using web addresses as names, so that data from different sources can be merged automatically.
RocqA long-established computer system, known until 2025 as Coq, in which people write mathematical proofs and verified software that the computer checks step by step.
rule-based systemA computer system that makes decisions by following a list of 'if… then…' rules written by people, rather than by learning from examples.
satisfiability modulo theoriesAn extension of logic-puzzle solving that also understands arithmetic, arrays and other data types, so it can check whether conditions in software, such as 'x + y > 10 and x < 3', can ever be met.
scriptIn 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.
semantic networkA 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.
semantic webAn effort to label data on the web in a standard, precise way so that programs, not only human readers, can tell what it means and connect facts drawn from many different sites.
simulated annealingAn optimization method inspired by slowly cooling metal: early on it sometimes accepts worse solutions, to escape dead ends, and becomes pickier as a 'temperature' setting gradually drops.
situation calculusA 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.
state space searchSolving a problem by treating every situation it could be in as a point on a map, with each possible move as a road between points, and searching for a route from the start to a goal.
STRIPSAn early AI planning program, and the simple action language it introduced, in which each action lists what must be true beforehand, what becomes true and what stops being true.
swarm intelligenceThe teamwork that appears when crowds of simple creatures or machines, such as ants, bees or small robots, each respond only to what is nearby, with nobody in charge, yet together solve problems.
symbolic regressionGetting a computer to discover a readable mathematical formula, such as a law of physics, that fits a set of measurements, instead of only fitting numbers into a formula chosen in advance.
tabu searchA search method that improves an answer by small changes while keeping a short memory of recent moves and forbidding their reversal for a while, so it does not circle back to the same dead ends.
temporal logicA logic for statements about time — 'eventually the light turns green', 'the door is never open while the train moves' — used to specify and check how systems must behave over time.
traveling salesman problemThe puzzle of planning a round trip through a list of cities that stops at each one once and covers the least distance. It is easy to state, but the number of possible routes explodes as cities are added.
unificationFinding values for the blanks in two logical patterns so that they become identical, for example matching 'parent(X, bob)' with 'parent(alice, Y)' by setting X to alice and Y to bob.
Web Ontology LanguageA web standard language for writing ontologies — precise definitions of categories and their relationships — so that software can check them for contradictions and infer new facts.
WikidataA free, shared collection of facts about millions of things — people, places, books, chemicals — kept by volunteers in the Wikipedia family and laid out so programs can use it as easily as people.

Also relevant

agentSomething that acts. In AI, a program that observes its situation and takes actions toward a goal: a game-playing learner, a software helper, or a chatbot that uses tools to complete tasks.
agent foundationsA research program that tried to work out, with mathematics, how an idealized reasoning agent should think and decide, as groundwork for building AI that stays safe.
AlphaGeometryA Google DeepMind system from 2024 that solves olympiad geometry problems nearly as well as top human competitors, by pairing a language model that suggests constructions with a logic engine that checks every step.
AlphaProofA Google DeepMind system that learned to write computer-checked mathematical proofs by trial and error. In 2024 it helped an AI reach silver-medal level at the International Mathematical Olympiad for the first time.
artificial lifeThe study of life by building lifelike systems — in computer simulations, robots or chemistry — to learn how living things organize themselves, reproduce and evolve, and what other kinds of life could exist.
beam searchA search method that keeps several of the best partial answers at each step, rather than only the single best, to find a better overall result without trying every possibility.
C4.5A widely used 1990s program that learns decision trees and rule sets from data; unlike its predecessor, it trims away unhelpful branches, copes with gaps in the data and can split on measured numbers.
ChinookA checkers program from the University of Alberta that in 1994 became the first program crowned world champion in a contest against people, and in 2007 proved that perfect checkers always ends in a draw.
computational intelligenceA family of AI methods inspired by nature, such as neural networks, fuzzy logic and evolutionary algorithms, that learn or adapt from numbers rather than following explicit logical rules.
computational theory of mindThe idea that thinking is a kind of computation: the mind processes information by following rules, much as a computer runs a program, and the brain is the machine doing it.
coreference resolutionWorking out which words in a text are talking about the same someone or something — for instance, that 'she', 'the senator' and 'Ms. Okafor' all mean one woman.
Dartmouth workshopA summer meeting of scientists at Dartmouth College in 1956, planned in a 1955 funding proposal that first used the phrase 'artificial intelligence'. Most histories treat it as the moment AI became a research field of its own.
decision treeA 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.
Deep BlueIBM's chess computer that beat world champion Garry Kasparov in 1997, the first time a machine defeated a reigning world champion in a match under standard tournament conditions.
DENDRALA 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.
do-calculusA set of mathematical rules, devised by Judea Pearl, for working out the effect of deliberately changing something using only observational data plus a diagram of what causes what.
Hubert DreyfusAn American philosopher who argued from the 1960s that computers following explicit rules could never match human intelligence, which rests on bodily, intuitive know-how.
Dreyfus's critique of AIThe 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.
dynamic programmingA problem-solving method that breaks a big problem into smaller overlapping pieces, solves each piece once and reuses the answers — the basis for computing the best long-term strategy step by step.
embedded agencyThe puzzle of how an agent should reason when it is itself part of the world it is trying to understand and change, rather than standing outside it.
expert systemA 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.
Edward FeigenbaumAn American computer scientist who pioneered expert systems, programs that capture specialists' knowledge as rules to solve problems in fields such as chemistry and medicine.
Jerry FodorAn American philosopher who argued that thinking is computation in an inner 'language of thought', and that neural networks cannot explain the structured nature of thought.
frame problemThe difficulty of getting an AI to work out what does and does not change when something happens, without spelling out every one of the countless things that stay the same.
General Problem SolverA 1950s program meant to solve any well-defined puzzle by repeatedly comparing where it was with where it wanted to be and choosing steps that closed the gap.
Gödel machineA purely theoretical design, proposed by Jürgen Schmidhuber, for a program allowed to change anything about itself, including how it learns, but only once it has proved that the change is an improvement.
Gödel's incompleteness theoremsTwo famous results from 1931 showing that any consistent set of rules for doing arithmetic leaves some true statements unprovable, and cannot prove that it is itself free of contradictions.
Ben GoertzelAn American AI researcher who made 'artificial general intelligence' a widely used name and built a research community around the goal of human-like, broadly capable AI.
GraphRAGA way of letting AI answer questions about a big pile of documents by first turning them into a map of people, things and their connections, then using that map to find and summarize answers.
guaranteed safe AIA proposed approach to AI safety in which systems come with mathematical proofs or checkable guarantees that they will not cause certain harms, instead of relying on testing alone.
John HollandAn American scientist who invented genetic algorithms, which solve problems by breeding and mutating populations of candidate answers, and helped found the science of complex systems.
ID3An early, influential method for automatically building a decision tree from examples, choosing at each step the question that most reduces uncertainty about the answer.
IJCAIA 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.
IMO Grand ChallengeThe goal of building an AI that earns a gold medal at the International Mathematical Olympiad, the top contest for school-age mathematicians. AI systems first reached gold-medal scores in 2025, though not under the challenge's strict original rules.
inferenceDrawing 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.
information extractionAutomatically pulling structured facts out of ordinary text — who did what, when, where and for how much — and putting them into a table or database.
integer programmingFinding the best plan when some decisions must be whole numbers or yes-or-no choices — you cannot open half a warehouse or send 2.7 trucks. That small change makes the best plan far harder to find.
intelligent agentIn AI theory, a decision-maker, such as a robot, a self-driving car, a chess program or a helpful piece of software, that notices what is going on around it and responds in ways that serve its goals.
language of thought hypothesisThe theory that we think in an inner, wordless mental language with its own symbols and grammar, which the brain manipulates when we reason, much as a computer manipulates code.
Douglas LenatAn American computer scientist who spent four decades building Cyc, a giant hand-written database of everyday common sense meant to let computers reason like people.
LispA programming language invented in 1958 for AI research, in which programs and data are written the same way, as nested lists in parentheses. It was the main language of AI for decades.
Lisp machineA 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.
Logic TheoristA 1956 computer program that proved theorems from a classic logic textbook by searching cleverly rather than trying every possibility. Many historians count it as the first AI program.
John McCarthyThe 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.
miniF2FA test of 488 math problems, mostly from competitions, written in a form a computer can check, used to measure how well AI systems produce proofs that are verified automatically.
Marvin MinskyAn 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.
MIT AI LabThe 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.
Monte Carlo tree searchA way for a game-playing program to choose a move by simulating many possible continuations, focusing on the most promising lines, and picking the move that worked out best on average.
motion planningWorking out how a robot should move from where it is to where it needs to be, without hitting anything and within the limits of what its body can do.
MRKL systemAn early design, pronounced 'miracle', in which a language model acts as a switchboard, sending each part of a question to the right specialist — a calculator, a database, a search tool — and combining the answers.
multi-agent systemA setup in which several AI agents, each with its own role or point of view, work together — or compete — on a problem, passing messages and results among themselves.
MYCINA 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.
natural language inferenceDeciding whether one sentence follows from another, contradicts it, or neither: if 'a man is playing a guitar on stage', is it true that 'a man is performing music'?
natural language understandingThe part of language AI concerned with working out what a piece of text means — who did what to whom, what the writer wants and what follows from it — rather than with producing text.
neats and scruffiesA joking split of AI researchers into 'neats', who want clean mathematical theories that can be proved correct, and 'scruffies', who build messy collections of tricks that simply work.
Allen NewellAn American computer scientist who, with Herbert Simon, wrote early AI programs that solved problems the way people do, and who sought a single theory of how the mind works.
Nils NilssonAn American computer scientist who co-created the A* search algorithm and the STRIPS planner for Shakey, the first robot to plan its own moves by reasoning, and wrote standard AI textbooks.
Peter NorvigAn American computer scientist who co-wrote the standard AI textbook, led research at Google, and helped show that simple methods trained on huge amounts of data often beat clever hand-built ones.
NP-completeDescribes problems, such as solving giant Sudokus, where a proposed answer is quick to check but no known method finds one quickly; a fast method for any one of them could be turned into one for all.
ontological crisisThe puzzle an AI faces when deeper understanding leaves goals phrased in its old way of seeing things without a clear meaning — like a rule to protect 'the same person' once it learns minds can be copied.
open-endednessThe ability of a system to keep producing new and interesting things without end — as evolution and human culture do — instead of stopping once it solves one fixed task.
path planningFinding a route for a robot or vehicle from a start to a goal that avoids obstacles, usually the shortest or safest one, much as a navigation app finds a route on a map.
Judea PearlAn Israeli-American computer scientist who gave AI the mathematics for reasoning under uncertainty and, later, for telling cause and effect apart from mere correlation.
plan-and-executeAn agent design that first writes out a complete step-by-step plan for a task, then carries out the steps one by one, revising the plan only when something goes wrong.
Polanyi's paradoxThe 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.
probabilistic roadmapA planning method that scatters many random safe robot poses through a space and links nearby ones into a road network, so routes can later be looked up quickly, as on a map.
PrologA 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.
PutnamBenchA test built from hundreds of problems from the Putnam, North America's top university math contest, written in a form a computer can check, used to measure how well AI systems prove harder mathematics.
rapidly exploring random treeA planning method that grows a branching tree of random moves outward from the robot's starting position, spreading quickly into open space until a branch reaches the goal.
reasoningWorking out conclusions step by step from facts, rules or evidence, as in solving a math problem or planning a trip. Whether today's AI truly reasons, or imitates reasoning, is debated.
Raj ReddyAn Indian-American computer scientist who pioneered computer speech recognition and large AI systems at Carnegie Mellon University, and shared the 1994 Turing Award.
Stuart RussellA British computer scientist who co-wrote the standard AI textbook and argues that AI must be redesigned to stay uncertain about what humans want, so that it remains under human control.
semantic parsingTranslating a sentence into a precise, machine-executable form — such as a database query or a logical formula — so that a computer can act on what was asked.
semantic searchSearch that understands what you mean, not just the words you typed, so a search for 'cheap places to stay' also finds 'budget hotels'.
Shakey the robotA wheeled robot built at a California research institute between 1966 and 1972 that could look around, make a plan and carry it out: the first mobile robot to work out its own steps toward a goal.
SHRDLUA program from around 1970 that let people move toy blocks in a simulated world by typing ordinary English commands and questions, and that could explain what it had done and why.
Herbert A. SimonAn American scientist of decision-making who, with Allen Newell, wrote some of the first AI programs and argued that minds and computers both think by manipulating symbols.
soft computingA group of computing methods, including fuzzy logic, neural networks and evolutionary algorithms, that accept imprecise, approximate answers in exchange for being practical on messy real-world problems.
subsumption architectureA way to build robot brains from stacked, reflex-like behaviors — a bottom layer that keeps clear of obstacles, others that roam or explore — where higher layers can take over from lower ones, with no central map or plan.
swarm roboticsCoordinating large groups of simple robots that follow local rules and talk only to their neighbors, so useful group behavior emerges without a central controller, as in ant colonies or bird flocks.
symbol grounding problemThe puzzle of how words or symbols inside a machine can mean anything by themselves, rather than only pointing to more symbols — like a dictionary that defines every word using other words.
syntactic parsingWorking out the grammatical structure of a sentence — which words group together and which word modifies which — usually drawn as a tree.
task decompositionBreaking a big, vague job into smaller, concrete steps that can each be done and checked — the way a recipe splits a meal into stages.
The Society of MindAI pioneer Marvin Minsky's 1986 book arguing that intelligence comes from huge numbers of tiny, simple processes working together, no one of which is smart by itself.
tree of thoughtsA method in which an AI explores several possible next steps of a problem, judges which look promising and backs out of dead ends, like a chess player weighing moves, instead of reasoning in one straight line.
Turing machineAn imaginary, extremely simple computer — a read-write head moving along an endless tape — that Alan Turing used in 1936 to define exactly what can be computed at all.
verifierA program or AI model that checks whether a proposed answer is correct — by running tests, comparing with a known result or judging the reasoning — so that good answers can be picked or rewarded.
Winograd schema challengeA test of machine common sense built from pairs of sentences in which changing one word changes what a pronoun refers to — something people resolve instantly using everyday knowledge.
Terry WinogradAn American computer scientist who built SHRDLU, a celebrated early program that conversed in English about a world of blocks, and later became a critic of symbolic AI.
word sense disambiguationWorking out which meaning of a word is intended in context — whether 'bank' means a riverside or a place that keeps money.
WordNetA hand-made map of English vocabulary in which words sharing a meaning are bundled together and the bundles are linked — recording, say, that a dog is a kind of animal and a wheel is part of a car.
XCONA 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.
Lotfi ZadehAn 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.