293:. Natural language programming is not to be mixed up with natural language interfacing or voice control where a program is first written and then communicated with through natural language using an interface added on. In NLP the functionality of a program is organised only for the definition of the meaning of sentences. For instance, NLP can be used to represent all the knowledge of an autonomous robot. Having done so, its tasks can be scripted by its users so that the robot can execute them autonomously while keeping to prescribed rules of behaviour as determined by the robot's user. Such robots are called
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X, guidance reference Xnow. Define the joint sliding surface G2 from the position velocity error Ve and angular velocity error Oe using the surface weights Alpha. Compute the smoothed sign function SG2 from the joint sliding surface G2 with sign threshold 0.01. Compute special dynamical force F from dynamical state X and surface weights Alpha. Compute control torque T and control force U from matrix J2, surface weights Alpha, special dynamical force F, smoothed sign function SG2. Finish conditional actions.
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If U_ is 'smc01-control', then do the following. Define surface weights Alpha as "". Initialise matrix Phi as a 'unit matrix'. Define J as the 'inertia matrix' of Spc01. Compute matrix J2 as the inverse of J. Compute position velocity error Ve and angular velocity error Oe from dynamical state
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in the sense as humans use concepts. Concepts in an NLP are examples (samples) of generic human concepts. Each sentence in a natural-language program is either (1) stating a relationship in a world model or (2) carries out an action in the environment or (3) carries out a computational procedure or
380:. The difference between these and NLP is that the latter builds up a single program or a library of routines that are programmed through natural language sentences using an ontology that defines the available data structures in a high level programming language.
412: – taxonomy – of concepts needed to describe tasks in the topic addressed. Each concept and all their attributes are defined in natural-language words. This ontology will define the data structures the NLP can use in sentences.
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that does not provide the details in any underlying high level programming language. In such an application the sentences used become high level abstractions (conceptualisations) of computing procedures that are computer language and machine independent.
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Repeating the previous step until you have no sentences left undefined. During this process each of sentences can be classified to belong to a section of the document to be produced in HTML or Latex format to form the final natural-language
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Researchers have started to experiment with natural language programming environments that use plain language prompts and then use AI (specifically large language models) to turn natural language into formal code. For example
Spatial Pixel
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description of some procedure that its author created. It is human readable and it can also be read by a suitable software agent. For example, a web page in an NLP format can be read by a software
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to turn natural language into P5.js code through OpenAI's API. In 2021 OpenAI developed a natural language programming environment for their programming large language model called
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The smallest unit of statement in NLP is a sentence. Each sentence is stated in terms of concepts from the underlying ontology, attributes in that ontology and named objects in
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Definition of one or more top-level sentences in terms of concepts from the ontology. These sentences are later used to invoke the most important activities in the topic.
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Providing a library of procedure calls (in the underlying high-level language) which are needed in the code definitions of some low-level-sentence meanings.
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agent to a person and she or he can ask the agent to execute some sentences, i.e. carry out some task or answer a question. There is a
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Veres, Sandor M. (2010). "Mission
Capable Autonomous Control Systems in the Oceans, in the Air and in Space".
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Pulido-Prieto, Oscar; Juárez-MartĂnez, Ulises (2017). "A Survey of
Naturalistic Programming Technologies".
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Defining each of the lower-level sentences in terms of other sentences or by a simple sentence of the form
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as their reasoning is transparent to users and this develops trust in robots. Natural language use and
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Natural
Language Programming of Agents and Robotic Devices: publishing for agents and humans in sEnglish
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processing of queries by sentences. This can allow interactive requests such as that implemented in
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Natural-language programming is a top-down method of writing software. Its stages are as follows:
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available for
English interpretation of HTML based NLP documents that a person can run on her
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Miller, L. A. (1981). "Natural language programming: Styles, strategies, and contrasts".
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Please help update this article to reflect recent events or newly available information.
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699:." Proceedings of the 38th International Conference on Software Engineering. ACM, 2016.
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where ... stands for a code in terms of the associated high-level programming language.
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Testing the meaning of each sentence by executing its code using testing objects.
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An example text from an
English language natural-language program is as follows:
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A set of NLP sentences, with associated ontology defined, can also be used as a
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Defining of each of the top-level sentences in terms of a sequence of sentences.
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Veres, S.M.; Molnar, L. (2010). "Documents for
Intelligent Agents in English".
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Publishing the natural-language program as a webpage on the
Internet or as a
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Computer knowledge representation format, system, methods, and applications
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Halpern, Mark (1990). "Natural
Language and Redundancy in Programming".
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893:. Studies in Computational Intelligence. Vol. 266. pp. 1–10.
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Providing a title, author data and compiling the sentences into an
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Compositional
Program Synthesis from Natural Language and Examples
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305:, a natural programming language for making interactive fiction,
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Binding Time: Six Studies in Programming Technology & Milieu
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491:(4) invokes an answering mechanism in response to a question.
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An ontology class is a natural-language program that is not a
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Contribution of natural-language programs to machine knowledge
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Publication value of natural-language programs and documents
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Raza, Mohammad, Sumit Gulwani, and Natasa Milic-Frayling. "
797:. Human-Computer Interaction Series. Vol. 9. pp.
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990: – a toolkit for training semantic parsers
828:. Ablex series in computational science. Intellect Books.
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natural programming language in the style of the plays of
791:"Feasibility Studies for Programming in Natural Language"
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881:(half written in sEnglish) by S M Veres an N K Lincoln,
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Programming language using English sentences in ASCII.
760:"On the foolishness of "natural language programming""
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Wolfram Alpha – how it works (part 2)
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Proc. TAROS’2008, Towards Autonomous Robotic Systems
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created an natural language programming environment
1008: – thoughts on how "natural" the
682:Development of reliable and trustworthy robots. "
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392:that defines a feedback control scheme using a
337:. In an NLP text every sentence unambiguously
879:Sliding mode control of autonomous spacecraft.
568:Programming languages with English-like syntax
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963:A Summary of the PSI Program Synthesis System
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325:are based on natural-language programming.
81:Learn how and when to remove these messages
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1006:How natural should a natural interface be?
1114:Programming in the large and in the small
254:Learn how and when to remove this message
200:Learn how and when to remove this message
138:Learn how and when to remove this message
854:Artificial Intelligence and Applications
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697:Program synthesis using natural language
462:A natural-language program is a precise
101:This article includes a list of general
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27:Language-oriented programming paradigm
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891:Brain-Inspired Information Technology
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789:Lieberman, Henry; Liu, Hugo (2006).
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503:AI in Natural Language Programming
107:it lacks sufficient corresponding
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62:This article has multiple issues.
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885:, Edinburgh, 1–3 September 2008.
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758:Dijkstra, Edsger W. (1979).
711:Computer Weekly, 4 June 2009
267:Natural-language programming
32:neuro-linguistic programming
18:Natural language programming
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1653:Parallel programming models
1627:Concurrent constraint logic
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581:Attempto Controlled English
552:Natural-language processing
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631:Structured Query Language
225:This article needs to be
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807:10.1007/1-4020-5386-x_20
547:Knowledge representation
536:Domain-specific language
30:Not to be confused with
965:." IJCAI. Vol. 5. 1977.
695:Desai, Aditya, et al. "
122:more precise citations.
1678:Structured concurrency
1063:Comparison by language
862:10.2316/p.2010.674-122
557:Source-code generation
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1059:Programming paradigms
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1889:Computer programming
1018:(dormant since 2005)
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795:End User Development
764:Program Construction
754:, London, June 2008.
542:End-user programming
423:Execute code "...".
394:sliding mode control
36:literate programming
1783:Self-modifying code
1391:Probabilistic logic
1322:Functional reactive
1277:Expression-oriented
1231:Partial application
670:10.1147/sj.202.0184
658:IBM Systems Journal
315:William Shakespeare
1884:Structured English
1696:Attribute-oriented
1469:List comprehension
1414:Algebraic modeling
1227:Anonymous function
1119:Design by contract
1089:Jackson structures
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684:transparent robots
468:personal assistant
345:in the underlying
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165:possibly contains
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947:Program synthesis
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871:978-0-88986-817-5
816:978-1-4020-4220-1
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1286:Concatenative
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319:Wolfram Alpha
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190:December 2014
183:
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163:This article
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128:December 2014
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19:
1833:Event-driven
1483:
1237:Higher-order
1165:Object-based
994:sysbrain.com
921:
917:
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882:
878:
853:
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763:
743:
731:Bibliography
716:
704:
691:
678:
661:
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506:
493:
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472:reader agent
471:
461:
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294:
281:in terms of
270:
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125:
106:
78:
71:
65:
64:Please help
61:
44:
1843:Intentional
1823:Data-driven
1805:of concerns
1764:Inferential
1751:Multi-stage
1731:Interactive
1608:Actor-based
1595:distributed
1538:Stack-based
1338:Synchronous
1295:Value-level
1282:Applicative
1199:Declarative
1157:Class-based
1000:wy-lang.org
924:(5): 1–35.
621:Software AG
576:AppleScript
496:pseudo code
374:interpreted
307:Shakespeare
279:programming
120:introducing
1873:Categories
1818:Components
1803:Separation
1778:Reflective
1772:by example
1716:Extensible
1590:Concurrent
1566:Production
1553:Templating
1533:Simulation
1518:Scientific
1438:Spacecraft
1366:Constraint
1361:Answer set
1313:Flow-based
1213:comparison
1208:Functional
1180:Persistent
1144:comparison
1109:Procedural
1081:Structured
1072:Imperative
643:References
626:Transcript
596:FLOW-MATIC
237:April 2023
174:improve it
103:references
67:improve it
1705:Inductive
1701:Automatic
1523:Scripting
1222:Recursive
616:SenseTalk
601:HyperTalk
591:ClearTalk
178:verifying
73:talk page
1858:Subjects
1848:Literate
1838:Features
1793:Template
1788:Symbolic
1760:Bayesian
1740:Hygienic
1600:parallel
1479:Modeling
1474:Low-code
1449:End-user
1386:Ontology
1318:Reactive
1305:Dataflow
1010:Ubiquity
633:(or SQL)
606:Inform 7
538:(or DSL)
520:See also
429:program.
410:ontology
396:method.
349:such as
339:compiles
311:esoteric
303:Inform 7
301:include
275:ontology
273:) is an
1813:Aspects
1721:Generic
1711:Dynamic
1570:Pattern
1548:Tactile
1513:Quantum
1503:filters
1434:Command
1333:Streams
1328:Signals
1099:Modular
938:2078759
799:459–473
488:concept
365:, etc.
341:into a
287:English
227:updated
172:Please
116:improve
1576:Visual
1543:System
1428:Action
1252:Strict
988:SEMPRE
936:
905:
868:
832:
813:
778:
750:
464:formal
363:Python
359:SciLab
355:Octave
351:MATLAB
317:, and
105:, but
1853:Roles
1736:Macro
1499:Pipes
1419:Array
1396:Query
1348:Logic
1257:GADTs
1247:Total
1170:Agent
934:S2CID
736:Books
637:xTalk
586:COBOL
514:Codex
446:file.
444:LaTeX
309:, an
38:, or
1501:and
1148:list
903:ISBN
866:ISBN
830:ISBN
811:ISBN
776:ISBN
748:ISBN
611:JOSS
440:HTML
1406:DSL
926:doi
895:doi
858:doi
803:doi
768:doi
686:" }
666:doi
451:PDF
442:or
271:NLP
176:by
1875::
1770:,
1766:,
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