| Revision as of 20:22, 4 November 2018 editWalter Görlitz (talk | contribs)Extended confirmed users, Pending changes reviewers294,571 edits Reverted good faith edits by ScienceAndEngineering (talk): Misplaced Pages copy-paste violations. (TW)Tag: Undo← Previous edit | Revision as of 20:24, 4 November 2018 edit undoWalter Görlitz (talk | contribs)Extended confirmed users, Pending changes reviewers294,571 edits Undid revision 867284232 by Walter Görlitz (talk) attribution was providedTags: Undo nowiki addedNext edit → | ||
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| == A == | == A == | ||
| *''']''' – Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some predicates to be incompletely defined, declared as abducible predicates. | |||
| *''']''' – | |||
| *''']''' – '''Abductive reasoning''' (also called '''abduction''',<ref name="Josephson">For example: {{cite book |editor1-last=Josephson |editor1-first=John R. |editor2-last=Josephson |editor2-first=Susan G. |date=1994 |title=Abductive Inference: Computation, Philosophy, Technology |location=Cambridge, UK; New York |publisher=Cambridge University Press |isbn=0521434610 |oclc=28149683 |doi=10.1017/CBO9780511530128}}</ref> '''abductive inference''',<ref name="Josephson"/> or '''retroduction'''<ref>{{Cite web|url = http://www.commens.org/dictionary/term/retroduction|title = <nowiki>Retroduction | Dictionary | Commens</nowiki>|date = |accessdate = 2014-08-24|website = Commens – Digital Companion to C. S. Peirce|publisher = Mats Bergman, Sami Paavola & João Queiroz}}</ref>) is a form of ] which starts with an observation or set of observations then seeks to find the simplest and most likely explanation. This process, unlike ], yields a plausible conclusion but does not ] it. | |||
| *''']''' – | |||
| *''']''' – is a ] for ]s, where a data type is defined by its behavior (]) from the point of view of a ''user'' of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations. | |||
| *''']''' – | |||
| *''']''' – is the process of removing physical, spatial, or temporal details<ref name=":1">{{Cite journal|last=Colburn|first=Timothy|last2=Shute|first2=Gary|date=2007-06-05|title=Abstraction in Computer Science|url=https://doi.org/10.1007/s11023-007-9061-7|journal=Minds and Machines|language=en|volume=17|issue=2|pages=169–184|doi=10.1007/s11023-007-9061-7|issn=0924-6495}}</ref> or ] in the study of objects or ] in order to more closely attend to other details of interest<ref name=":0">{{Cite journal|last=Kramer|first=Jeff|date=2007-04-01|title=Is abstraction the key to computing?|url=http://dl.acm.org/citation.cfm?id=1232743.1232745|journal=Communications of the ACM|volume=50|issue=4|pages=36–42|doi=10.1145/1232743.1232745|issn=0001-0782}}</ref> | |||
| *''']''' – | |||
| *''']''' – is a perceived increase in the rate of ] throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change. | |||
| *''']''' – | |||
| *''']''' – is a language for specifying ]s, and is commonly used to create ]s of the effects of actions on the world.<ref>Michael Gelfond, Vladimir Lifschitz (1998) "", ''Linköping Electronic Articles in Computer and Information Science'', vol '''3''', nr ''16''.</ref> Action languages are commonly used in the ] and ] domains, where they describe how actions affect the states of systems over time, and may be used for ]. | |||
| *''']''' – | |||
| *''']''' – is an area of machine learning concerned with creation and modification of software agent's knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners. | |||
| *''']''' – | |||
| *''']''' – is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. | |||
| *''']''' – | |||
| *''']''' – | *''']''' – | ||
| *''']''' – an algorithm that changes its behavior at the time it is run, based on ''a priori'' defined reward mechanism or criterion. | *''']''' – an algorithm that changes its behavior at the time it is run, based on ''a priori'' defined reward mechanism or criterion. | ||
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| == References == | == References == | ||
| <references /> | |||
Revision as of 20:24, 4 November 2018
Most of the terms listed in Misplaced Pages glossaries are already defined and explained within Misplaced Pages itself. However, glossaries like this one are useful for looking up, comparing and reviewing large numbers of terms together. You can help enhance this page by adding new terms or writing definitions for existing ones.
This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.
| Part of a series on |
| Artificial intelligence |
|---|
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| Major goals |
| Approaches |
| Applications |
| Philosophy |
| History |
| Glossary |
A
- Abductive logic programming – Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some predicates to be incompletely defined, declared as abducible predicates.
- Abductive reasoning – Abductive reasoning (also called abduction, abductive inference, or retroduction) is a form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it.
- Abstract data type – is a mathematical model for data types, where a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations.
- Abstraction – is the process of removing physical, spatial, or temporal details or attributes in the study of objects or systems in order to more closely attend to other details of interest
- Accelerating change – is a perceived increase in the rate of technological change throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change.
- Action language – is a language for specifying state transition systems, and is commonly used to create formal models of the effects of actions on the world. Action languages are commonly used in the artificial intelligence and robotics domains, where they describe how actions affect the states of systems over time, and may be used for automated planning.
- Action model learning – is an area of machine learning concerned with creation and modification of software agent's knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners.
- Action selection – is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment.
- Activation function –
- Adaptive algorithm – an algorithm that changes its behavior at the time it is run, based on a priori defined reward mechanism or criterion.
- Adaptive neuro fuzzy inference system –
- Admissible heuristic –
- Affective computing –
- Agent architecture –
- AI accelerator –
- AI-complete –
- Algorithm – is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing and automated reasoning tasks.
- Algorithmic efficiency –
- Algorithmic probability –
- AlphaGo –
- Ambient intelligence –
- Analysis of algorithms –
- Analytics – the discovery, interpretation, and communication of meaningful patterns in data.
- Answer set programming –
- Anytime algorithm – an algorithm that can return a valid solution to a problem even if it is interrupted before it ends.
- Application programming interface –
- Approximate string matching –
- Approximation error –
- Argumentation framework –
- Artificial immune system –
- Artificial intelligence –
- Artificial Intelligence Markup Language –
- Artificial neural network –
- Association for the Advancement of Artificial Intelligence –
- Asymptotic computational complexity –
- Attributional calculus –
- Augmented reality –
- Automata theory –
- Automated planning and scheduling –
- Automated reasoning –
- Autonomic computing –
- Autonomous car –
- Autonomous robot –
B
- Backpropagation –
- Backpropagation through time –
- Backward chaining –
- Bag-of-words model –
- Bag-of-words model in computer vision –
- Batch normalization –
- Bayesian programming –
- Bees algorithm –
- Behavior informatics –
- Behavior tree –
- Belief-desire-intention software model –
- Bias–variance tradeoff –
- Big data –
- Big O notation –
- Binary tree –
- Bio-inspired computing –
- Blackboard system –
- Boltzmann machine –
- Boolean satisfiability problem –
- Brain technology –
- Branching factor –
- Brute-force search –
C
- Capsule neural network –
- Case-based reasoning –
- Chatbot –
- Cloud robotics –
- Cluster analysis –
- Cobweb –
- Cognitive architecture –
- Cognitive computing –
- Cognitive science –
- Combinatorial optimization –
- Committee machine –
- Commonsense knowledge –
- Commonsense reasoning –
- Computational chemistry –
- Computational complexity theory –
- Computational creativity –
- Computational cybernetics –
- Computational humor –
- Computational intelligence –
- Computational learning theory –
- Computational linguistics –
- Computational mathematics – the mathematical research in areas of science where computing plays an essential role.
- Computational neuroscience –
- Computational number theory – also known as algorithmic number theory, it is the study of algorithms for performing number theoretic computations.
- Computational problem –
- Computational statistics –
- Computational vision –
- Computer-automated design –
- Computer science –
- Computer vision –
- Concept drift –
- Confusion matrix –
- Connectionism –
- Consistent heuristic –
- Constrained conditional model –
- Constraint logic programming –
- Constraint programming –
- Constructed language –
- Control theory –
- Convolutional neural network –
- Crossover –
D
- Darkforest –
- Dartmouth workshop –
- Data fusion –
- Data integration –
- Data mining –
- Data science –
- Data set –
- Data warehouse –
- Datalog –
- Decision boundary –
- Decision support system –
- Decision theory –
- Decision tree learning –
- Declarative programming –
- Deductive classifier –
- Deep Blue –
- Deep learning –
- Default logic –
- Description logic –
- Developmental robotics –
- Diagnosis –
- Dialog system –
- Dimensionality reduction –
- Discrete system –
- Distributed artificial intelligence –
- Dynamic epistemic logic –
E
- Eager learning –
- Ebert test –
- Echo state network –
- Embodied agent –
- Embodied cognitive science –
- Error-driven learning –
- Ensemble averaging –
- Ethics of artificial intelligence –
- Evolutionary algorithm –
- Evolutionary computation –
- Evolving classification function –
- Existential risk –
- Expert systems –
F
- Fast-and-frugal trees –
- Feature extraction –
- Feature learning –
- Feature selection –
- First-order logic –
- Fluent –
- Formal language –
- Forward chaining –
- Frame –
- Frame language –
- Frame problem –
- Friendly artificial intelligence –
- Futures studies –
- Fuzzy control system –
- Fuzzy logic –
- Fuzzy rule –
- Fuzzy set –
G
- Game theory –
- Genetic algorithm –
- Genetic operator –
- Glowworm swarm optimization –
- Google DeepMind –
- Graph –
- Graph –
- Graph database –
- Graph theory –
- Graph traversal –
H
- Heuristic –
- Hidden layer – an internal layer of neurons in an artificial neural network, not dedicated to input or output
- Hidden unit – an neuron in a hidden layer in an artificial neural network
- Hyper-heuristic –
I
- IEEE Computational Intelligence Society –
- Incremental learning –
- Inference engine –
- Information integration –
- Information Processing Language –
- Intelligence amplification –
- Intelligence explosion –
- Intelligent agent –
- Intelligent control –
- Intelligent personal assistant –
- Interpretation –
- Issue trees –
J
K
- Kernel method –
- KL-ONE –
- Knowledge acquisition –
- Knowledge-based systems –
- Knowledge engineering –
- Knowledge extraction –
- Knowledge Interchange Format –
- Knowledge representation and reasoning –
L
M
- Machine vision –
- Markov chain –
- Markov decision process –
- Mathematical optimization –
- Machine learning –
- Machine listening –
- Machine perception –
- Mechanism design –
- Mechatronics –
- Metabolic network modelling –
- Metaheuristic –
- Model checking –
- Modus ponens –
- Modus tollens –
- Monte Carlo tree search –
- Multi-agent system –
- Multi-swarm optimization –
- Mutation –
- Mycin –
N
- Naive Bayes classifier –
- Naive semantics –
- Name binding –
- Named-entity recognition –
- Named graph –
- Natural language generation –
- Natural language processing –
- Natural language programming –
- Network motif –
- Neural machine translation –
- Neural Turing machine –
- Neuro-fuzzy –
- Neurocybernetics –
- Neuromorphic engineering –
- Node –
- Nondeterministic algorithm –
- Nouvelle AI –
- NP –
- NP-completeness –
- NP-hardness –
O
- Occam's razor –
- Offline learning –
- Online learning –
- Ontology engineering –
- Ontology learning –
- OpenAI –
- OpenCog –
- Open Mind Common Sense –
- Open-source software –
P
- Partial order reduction –
- Partially observable Markov decision process –
- Particle swarm optimization –
- Pathfinding –
- Pattern recognition –
- Planner –
- Predicate logic –
- Predictive analytics –
- Principal component analysis –
- Principle of rationality –
- Probabilistic programming language –
- Production Rule Representation –
- Production system –
- Programming language –
- Prolog –
- Propositional calculus –
- Python –
Q
R
- R programming language –
- Radial basis function network –
- Random forest –
- Reasoning system –
- Recurrent neural network –
- Region connection calculus –
- Reinforcement learning –
- Reservoir computing –
- Resource Description Framework –
- Restricted Boltzmann machine –
- Rete algorithm –
- Robotics –
- Rule-based system –
S
- Satisfiability –
- Search algorithm –
- Selection –
- Self-management –
- Semantic network –
- Semantic reasoner –
- Semantic query –
- Semantics –
- Sensor fusion –
- Separation logic –
- Similarity learning –
- Simulated annealing –
- Situated approach –
- Situation calculus –
- SLD resolution –
- Soft computing –
- Software –
- Software engineering –
- Spatial-temporal reasoning –
- SPARQL –
- Speech recognition –
- Spiking neural network –
- State –
- Statistical classification –
- Statistical relational learning –
- Stochastic optimization –
- Stochastic semantic analysis
- STRIPS –
- Subject-matter expert –
- Superintelligence –
- Supervised learning –
- Swarm intelligence –
- Symbolic artificial intelligence –
- Synthetic intelligence –
- Systems neuroscience –
T
- Technological singularity –
- Temporal difference learning –
- Tensor network theory –
- TensorFlow –
- Theoretical computer science –
- Theory of computation –
- Thompson sampling –
- Time complexity –
- Transhumanism –
- Transition system –
- Tree traversal –
- True quantified Boolean formula –
- Turing test –
- Type system –
U
V
W
X
Y
Z
Contents:See also
References
- ^ For example: Josephson, John R.; Josephson, Susan G., eds. (1994). Abductive Inference: Computation, Philosophy, Technology. Cambridge, UK; New York: Cambridge University Press. doi:10.1017/CBO9780511530128. ISBN 0521434610. OCLC 28149683.
- "Retroduction | Dictionary | Commens". Commens – Digital Companion to C. S. Peirce. Mats Bergman, Sami Paavola & João Queiroz. Retrieved 24 August 2014.
- Colburn, Timothy; Shute, Gary (5 June 2007). "Abstraction in Computer Science". Minds and Machines. 17 (2): 169–184. doi:10.1007/s11023-007-9061-7. ISSN 0924-6495.
- Kramer, Jeff (1 April 2007). "Is abstraction the key to computing?". Communications of the ACM. 50 (4): 36–42. doi:10.1145/1232743.1232745. ISSN 0001-0782.
- Michael Gelfond, Vladimir Lifschitz (1998) "Action Languages", Linköping Electronic Articles in Computer and Information Science, vol 3, nr 16.
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