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Journal articles on the topic 'Declarative programming'

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Published: 4 June 2021
Last updated: 30 July 2024

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1

Crossley, John N. "Declarative programming." Electronic Notes in Theoretical Computer Science 61 (January 2002): 20. http://dx.doi.org/10.1016/s1571-0661(04)00302-0.

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BáRány, Vince, Balder Ten Cate, Benny Kimelfeld, Dan Olteanu, and Zografoula Vagena. "Declarative Probabilistic Programming with Datalog." ACM Transactions on Database Systems 42, no. 4 (November 13, 2017): 1–35. http://dx.doi.org/10.1145/3132700.

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3

Hanus, Michael. "Reduction Strategies for Declarative Programming." Electronic Notes in Theoretical Computer Science 57 (December 2001): 184–91. http://dx.doi.org/10.1016/s1571-0661(04)00273-7.

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4

Komorowski, Henryk Jan. "A declarative logic programming environment." Journal of Systems and Software 8, no. 2 (March 1988): 77–89. http://dx.doi.org/10.1016/0164-1212(88)90002-7.

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5

Lloyd, J. W., and K. S. Ng. "Declarative programming for agent applications." Autonomous Agents and Multi-Agent Systems 23, no. 2 (June 8, 2010): 224–72. http://dx.doi.org/10.1007/s10458-010-9138-1.

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6

Schor, Marshall I. "Declarative Knowledge Programming: Better Than Procedural?" IEEE Expert 1, no. 1 (April 1986): 36–43. http://dx.doi.org/10.1109/mex.1986.5006497.

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7

Louridas, Panagiotis. "Declarative GUI Programming in Microsoft Windows." IEEE Software 24, no. 4 (July 2007): 16–19. http://dx.doi.org/10.1109/ms.2007.105.

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8

Lloyd, John W. "Declarative programming for artificial intelligence applications." ACM SIGPLAN Notices 42, no. 9 (October 2007): 123–24. http://dx.doi.org/10.1145/1291220.1291152.

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9

Finkelstein, Stacy E., Peter Freyd, and James Lipton. "A new framework for declarative programming." Theoretical Computer Science 300, no. 1-3 (May 2003): 91–160. http://dx.doi.org/10.1016/s0304-3975(01)00308-5.

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Echahed, Rachid, and Wendelin Serwe. "Defining Actions in Concurrent Declarative Programming." Electronic Notes in Theoretical Computer Science 64 (September 2002): 176–94. http://dx.doi.org/10.1016/s1571-0661(04)80350-5.

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HOFSTEDT, PETRA, and PETER PEPPER. "Integration of declarative and constraint programming." Theory and Practice of Logic Programming 7, no. 1-2 (January 2007): 93–121. http://dx.doi.org/10.1017/s1471068406002833.

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AbstractCombining a set of existing constraint solvers into an integrated system of cooperating solvers is a useful and economic principle to solve hybrid constraint problems. In this paper we show that this approach can also be used to integrate different language paradigms into a unified framework. Furthermore, we study the syntactic, semantic and operational impacts of this idea for the amalgamation of declarative and constraint programming.
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Chen, Adam, Parisa Fathololumi, Eric Koskinen, and Jared Pincus. "Veracity: declarative multicore programming with commutativity." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (October 31, 2022): 1726–56. http://dx.doi.org/10.1145/3563349.

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There is an ongoing effort to provide programming abstractions that ease the burden of exploiting multicore hardware. Many programming abstractions ( e.g. , concurrent objects, transactional memory, etc.) simplify matters, but still involve intricate engineering. We argue that some difficulty of multicore programming can be meliorated through a declarative programming style in which programmers directly express the independence of fragments of sequential programs. In our proposed paradigm, programmers write programs in a familiar, sequential manner, with the added ability to explicitly express the conditions under which code fragments sequentially commute. Putting such commutativity conditions into source code offers a new entry point for a compiler to exploit the known connection between commutativity and parallelism. We give a semantics for the programmer’s sequential perspective and, under a correctness condition, find that a compiler-transformed parallel execution is equivalent to the sequential semantics. Serializability/linearizability are not the right fit for this condition, so we introduce scoped serializability and show how it can be enforced with lock synthesis techniques. We next describe a technique for automatically verifying and synthesizing commute conditions via a new reduction from our commute blocks to logical specifications, upon which symbolic commutativity reasoning can be performed. We implemented our work in a new language called Veracity, implemented in Multicore OCaml. We show that commutativity conditions can be automatically generated across a variety of new benchmark programs, confirm the expectation that concurrency speedups can be seen as the computation increases, and apply our work to a small in-memory filesystem and an adaptation of a crowdfund blockchain smart contract.
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Hewett, Rattikorn, Phongphun Kijsanayothin, Stephanie Bak, and Marry Galbrei. "Cybersecurity policy verification with declarative programming." Applied Intelligence 45, no. 1 (January 26, 2016): 83–95. http://dx.doi.org/10.1007/s10489-015-0749-8.

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14

Guns, Tias. "Declarative pattern mining using constraint programming." Constraints 20, no. 4 (September 14, 2015): 492–93. http://dx.doi.org/10.1007/s10601-015-9220-z.

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15

Kosky, Anthony. "Declarative Languages for Advanced Information Technologies." Journal of Information Technology 3, no. 2 (June 1988): 110–18. http://dx.doi.org/10.1177/026839628800300208.

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In recent times there has been a great increase in interest in declarative programming languages, which are based on sound mathematical formalisms, independent of the machinery on which they are implemented. Such languages are easier to understand and debug, and have advantages with regard to program transformation and verification, and parallel implementation over conventional, imperative languages. In this essay I will discuss the various paradigms available for declarative programming, and also the object orientated programming paradigm, and put forward various ideas that I feel are of particular interest.
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16

Kruglov, Leonid, and Yury Brodsky. "MODEL-ORIENTED PROGRAMMING." Proceedings of CBU in Natural Sciences and ICT 2 (October 24, 2021): 63–67. http://dx.doi.org/10.12955/pns.v2.154.

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The problem of complex multi-component system processing arises in many fields of science and engineering. A system can be described in terms of its components, behavior, and interaction. This work proposes a new declarative Turing complete “model-oriented” programming paradigm based on the concept of “model-component” - a complex structure with well-defined characteristics and behavior, and no external methods. The set of model-components is closed under the union operation of model-components into “model-complex”. The proposed approach allows the program to describe the complex system and behavior of its components in a declarative way, possesses a higher level of encapsulation than the object-oriented paradigm, involves a reduced amount of imperative programming, and is naturally focused on parallel computations.
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Gebser, Martin, Tomi Janhunen, and Jussi Rintanen. "Declarative encodings of acyclicity properties." Journal of Logic and Computation 30, no. 4 (September 8, 2015): 923–52. http://dx.doi.org/10.1093/logcom/exv063.

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Abstract Many knowledge representation tasks involve trees or similar structures as abstract datatypes. However, devising compact and efficient declarative representations of such structural properties is non-obvious and can be challenging indeed. In this article, we take a number of acyclicity properties into consideration and investigate various logic-based approaches to encode them. We use answer set programming as the primary representation language but also consider mappings to related formalisms, such as propositional logic, difference logic and linear programming. We study the compactness of encodings and the resulting computational performance on benchmarks involving acyclic or tree structures.
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18

Golemanova, Emilia, and Tzanko Golemanov. "Genetic Algorithms in a Visual Declarative Programming." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 19 (June 21, 2022): 138–52. http://dx.doi.org/10.37394/23209.2022.19.14.

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mperative languages like Java, C++, and Python are mostly used for the implementation of Genetic algorithms (GA). Other programming paradigms are far from being an object of study. The paper explores the advantages of a new non-mainstream programming paradigm, with declarative and nondeterministic features, in the implementation of GA. Control Network Programming (CNP) is a visual declarative style of programming in which the program is a set of recursive graphs, that are graphically visualized and developed. The paper demonstrates how the GA can be implemented in an automatic, i.e. non-procedural (declarative) way, using the built-in CNP inference mechanism and tools for its control. The CNP programs are easy to develop and comprehend, thus, CNP can be considered a convenient programming paradigm for efficient teaching and learning of nondeterministic, heuristic, and stochastic algorithms, and in particular GA. The outcomes of using CNP in delivering a course on Advanced Algorithm Design are shown and analyzed, and they strongly support the positive results in teaching when CNP is applied.
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19

STAVYTSKYI, Pavlo, and Viktoriia VOITKO. "METHOD OF THE DECLARATIVE METAPROGRAMMING BASED ON DOMAIN-SPECIFIC PROGRAMMING LANGUAGES." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 249–55. http://dx.doi.org/10.31891/2307-5732-2022-311-4-249-255.

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A concept of declarative metaprogramming which focuses on program code generation is considered. The method is based on using domain-specific languages that are implemented by means of existing general-purpose programming languages. Since metaprogramming is based on using programs that produce other programs, the declarative approach means that the input and the output program code must be as close as possible in terms of syntax or ideally identical. In order to generate a program code, it is required to write templates that hold the exact structure of a resulting generated code but contain placeholders for specifics of concrete generated instances. Such an approach is highly useful when there is a need of generating a large number of similar program files by structure but different in details. The simplest way to define such templates would be just by constructing them from string literals by injecting the specific details using a string interpolation mechanism for example. However, such an approach does not provide type safety and compile time validation, so there is no guarantee the generated code is a correct and compilable program. The domain-specific language, on the other hand, replicates all the syntactic constructions of the target programming language so that, the resulting generated code would be the same as code written by the programmer. Moreover, having preserved all the syntax validations by the compiler on which the domain-specific language is based. The paper considers a concrete example of a code generator for a Starlark programming language, a dialect of Python while the domain specific-language is based on Kotlin language. Using the toolset of Kotlin, such a domain-specific language replicates such Starlark constructs as variable assignments, list comprehensions, library functions, slices, etc with a required level of customizability.
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20

Watson, I., V. Woods, P. Watson, R. Banach, M. Greenberg, and J. Sargeant. "Flagship: a parallel architecture for declarative programming." ACM SIGARCH Computer Architecture News 16, no. 2 (May 17, 1988): 124–30. http://dx.doi.org/10.1145/633625.52415.

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21

Mens, Tom, Roel Wuyts, Kris De Volder, and Kim Mens. "Declarative Meta Programming to Support Software Development." ACM SIGSOFT Software Engineering Notes 28, no. 2 (March 2003): 1. http://dx.doi.org/10.1145/638750.638770.

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22

Myers, Brad A., Dario A. Giuse, and Brad Vander Zanden. "Declarative programming in a prototype-instance system." ACM SIGPLAN Notices 27, no. 10 (October 31, 1992): 184–200. http://dx.doi.org/10.1145/141937.141953.

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23

Sivaramakrishnan, KC, Gowtham Kaki, and Suresh Jagannathan. "Declarative programming over eventually consistent data stores." ACM SIGPLAN Notices 50, no. 6 (August 7, 2015): 413–24. http://dx.doi.org/10.1145/2813885.2737981.

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24

Rossi, Gianfranco. "Set-based Nondeterministic Declarative Programming in Singleton." Electronic Notes in Theoretical Computer Science 76 (November 2002): 216–32. http://dx.doi.org/10.1016/s1571-0661(04)80795-3.

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25

Bakay, Á., L. Madarász, A. Hinsenkamp, Z. Papp, and T. P. Dobrowiecki. "Declarative Programming Tools for Fermentor Control System." IFAC Proceedings Volumes 23, no. 8 (August 1990): 293–97. http://dx.doi.org/10.1016/s1474-6670(17)51839-x.

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26

DAGNINO, FRANCESCO, DAVIDE ANCONA, and ELENA ZUCCA. "Flexible coinductive logic programming." Theory and Practice of Logic Programming 20, no. 6 (September 22, 2020): 818–33. http://dx.doi.org/10.1017/s147106842000023x.

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AbstractRecursive definitions of predicates are usually interpreted either inductively or coinductively. Recently, a more powerful approach has been proposed, called flexible coinduction, to express a variety of intermediate interpretations, necessary in some cases to get the correct meaning. We provide a detailed formal account of an extension of logic programming supporting flexible coinduction. Syntactically, programs are enriched by coclauses, clauses with a special meaning used to tune the interpretation of predicates. As usual, the declarative semantics can be expressed as a fixed point which, however, is not necessarily the least, nor the greatest one, but is determined by the coclauses. Correspondingly, the operational semantics is a combination of standard SLD resolution and coSLD resolution. We prove that the operational semantics is sound and complete with respect to declarative semantics restricted to finite comodels.
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27

Rizzo, Nicola, and Agostino Dovier. "3coSoKu and its declarative modeling." Journal of Logic and Computation 32, no. 2 (January 18, 2022): 307–30. http://dx.doi.org/10.1093/logcom/exab086.

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Abstract In this paper, we analyze the physical puzzle IcoSoKu, a game about placing some given triangular tiles on the faces of an icosahedron in order to fill the capacities of its vertices, and we propose its generalization called 3coSoKu, admitting an arbitrary playing field with triangular faces, arbitrary capacities and an arbitrary set of triangular tiles. First, we prove the strong NP-completeness of 3coSoKu, even when the playing field is a convex polyhedron with equilateral triangles as faces. Second, we encode 3coSoKu both in the constraint modeling language MiniZinc and in the logic programming paradigm known as Answer Set Programming and we develop a visual tool for an accessible interface to the solver. Finally, we use our encodings to verify experimentally that every initial state for IcoSoKu admits a solution.
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28

COSTANTINI, STEFANIA. "SEMANTICS OF A METALOGIC PROGRAMMING LANGUAGE." International Journal of Foundations of Computer Science 01, no. 03 (September 1990): 233–47. http://dx.doi.org/10.1142/s0129054190000175.

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This paper presents the declarative and procedural semantics of Reflective Prolog, a new logic language able to represent metaknowledge and use it in the proof process via an extended resolution procedure including forms of implicit reflection. The declarative semantics of a Reflective Prolog definite program is provided in terms of the Least Reflective Herbrand Model of the program, characterized by means of a suitable mapping. The extended resolution is then shown to be sound and complete with respect to the Least Reflective Herband Model.
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WIKAREK, Jarosław, Paweł SITEK, and Mieczysław JAGODZIŃSKI. "A DECLARATIVE APPROACH TO SHOP ORDERS OPTIMIZATION." Applied Computer Science 15, no. 4 (December 30, 2019): 5–15. http://dx.doi.org/10.35784/acs-2019-25.

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The paper presents the problem of material requirements planning with optimization of load distribution between work centers and workers’ groups. Moreover, it discusses the computational example for shop orders optimization. The data for this example were taken from the relational database. The method of Constraint Logic Programming (CLP) for shop orders optimization has been suggested. Using Constraint Logic Programming, the constraints may be directly introduced to the problem declaration, which is equivalent to the source code of the program. The ECLiPSe-CLP software system has been presented. It allows for solving optimization problems concerning dimensions greater than in the case of the professional mathematical programming solver “LINGO”. The application of ECLiPSe-CLP in accessing data from relational databases has been presented.
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Calimeri, Francesco, Giovambattista Ianni, Thomas Krennwallner, and Francesco Ricca. "The Answer Set Programming Competition." AI Magazine 33, no. 4 (December 21, 2012): 114. http://dx.doi.org/10.1609/aimag.v33i4.2448.

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The Answer Set Programming (ASP) Competition is a biannual event for evaluating declarative knowledge representation systems on hard and demanding AI problems. The competition consists of two main tracks: the ASP system track and the model and solve track. The traditional system track compares dedicated answer set solvers on ASP benchmarks, while the model and solve track invites any researcher and developer of declarative knowledge representation systems to participate in an open challenge for solving sophisticated AI problems with their tools of choice. This article provides an overview of the ASP competition series, reviews its origins and history, giving insights on organizing and running such an elaborate event, and briefly discusses about the lessons learned so far.
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31

Keane, John A. "An overview of the Flagship system." Journal of Functional Programming 4, no. 1 (January 1994): 19–45. http://dx.doi.org/10.1017/s0956796800000927.

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AbstractThe Flagship Project1 was a research collaboration between the University of Manchester, Imperial College London and International Computers Ltd. The project was unusual in that it aimed to produce a complete computing system based on a declarative programming style. Three areas of a declarative system were addressed: (1) programming languages and programming environments; (2) the machine architecture and computational models; and (3) the software environment. This overview paper discusses each of these areas, the intention being to present the project as a coherent whole.
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32

HANUS, MICHAEL, and SVEN KOSCHNICKE. "An ER-based framework for declarative web programming." Theory and Practice of Logic Programming 14, no. 3 (October 30, 2012): 269–91. http://dx.doi.org/10.1017/s1471068412000385.

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AbstractWe describe a framework to support the implementation of web-based systems intended to manipulate data stored in relational databases. Since the conceptual model of a relational database is often specified as an entity-relationship (ER) model, we propose to use the ER model to generate a complete implementation in the declarative programming language Curry. This implementation contains operations to create and manipulate entities of the data model, supports authentication, authorization, session handling, and the composition of individual operations to user processes. Furthermore, the implementation ensures the consistency of the database w.r.t. the data dependencies specified in the ER model, i.e., updates initiated by the user cannot lead to an inconsistent state of the database. In order to generate a high-level declarative implementation that can be easily adapted to individual customer requirements, the framework exploits previous works on declarative database programming and web user interface construction in Curry.
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33

Norilo, Vesa. "Kronos: A Declarative Metaprogramming Language for Digital Signal Processing." Computer Music Journal 39, no. 4 (December 2015): 30–48. http://dx.doi.org/10.1162/comj_a_00330.

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Kronos is a signal-processing programming language based on the principles of semifunctional reactive systems. It is aimed at efficient signal processing at the elementary level, and built to scale towards higher-level tasks by utilizing the powerful programming paradigms of “metaprogramming” and reactive multirate systems. The Kronos language features expressive source code as well as a streamlined, efficient runtime. The programming model presented is adaptable for both sample-stream and event processing, offering a cleanly functional programming paradigm for a wide range of musical signal-processing problems, exemplified herein by a selection and discussion of code examples.
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34

Appeltauer, Malte, Robert Hirschfeld, and Jens Lincke. "Declarative Layer Composition with The JCop Programming Language." Journal of Object Technology 12, no. 2 (2013): 4:1. http://dx.doi.org/10.5381/jot.2013.12.2.a4.

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35

Bujosa, Andrés, and Regino Criado. "SYNTACTIC ELEMENTS OF DECLARATIVE PROGRAMMING: SYMBOLIC LINEAR EQUATIONS." Fundamenta Informaticae 25, no. 1 (1996): 39–48. http://dx.doi.org/10.3233/fi-1996-25104.

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36

Greenberg, Mark, and Viv Woods. "Flagship—a parallel reduction machine for declarative programming." Computing & Control Engineering Journal 1, no. 2 (1990): 81. http://dx.doi.org/10.1049/cce:19900022.

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37

Yan, Song Yuan. "Foundations of declarative testing in arbitrary logic programming." International Journal of Computer Mathematics 34, no. 3-4 (January 1990): 145–60. http://dx.doi.org/10.1080/00207169008803872.

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38

Negreanu, Lorina. "Declarative Programming with Temporal Constraints, in the LanguageCG." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/540854.

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Specifying and interpreting temporal constraints are key elements of knowledge representation and reasoning, with applications in temporal databases, agent programming, and ambient intelligence. We present and formally characterize the languageCG, which tackles this issue. InCG, users are able to develop time-dependent programs, in a flexible and straightforward manner. Such programs can, in turn, be coupled with evolving environments, thus empowering users to control the environment’s evolution.CGrelies on a structure for storing temporal information, together with a dedicated query mechanism. Hence, we explore the computational complexity of our query satisfaction problem. We discuss previous implementation attempts ofCGand introduce a novel prototype which relies on logic programming. Finally, we address the issue of consistency and correctness ofCGprogram execution, using the Event-B modeling approach.
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39

Yuan Yan, Song. "Foundations of declarative debugging in arbitrary logic programming." International Journal of Man-Machine Studies 32, no. 2 (February 1990): 215–32. http://dx.doi.org/10.1016/s0020-7373(05)80047-1.

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40

Finkelstein, Stacy E., Peter Freyd, and James Lipton. "Erratum to: “A new framework for declarative programming”." Theoretical Computer Science 311, no. 1-3 (January 2004): 527. http://dx.doi.org/10.1016/j.tcs.2003.09.011.

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41

Amato, Gianluca, James Lipton, and Robert McGrail. "On the algebraic structure of declarative programming languages." Theoretical Computer Science 410, no. 46 (November 2009): 4626–71. http://dx.doi.org/10.1016/j.tcs.2009.07.038.

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42

Savnik, Iztok, Tomaž Mohorič, and Vanja Josifovski. "Extending database programming language with declarative querying facilities." Microprocessing and Microprogramming 40, no. 10-12 (December 1994): 905–8. http://dx.doi.org/10.1016/0165-6074(94)90066-3.

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43

Lifschitz, Vladimir. "Answer Sets and the Language of Answer Set Programming." AI Magazine 37, no. 3 (October 7, 2016): 7–12. http://dx.doi.org/10.1609/aimag.v37i3.2670.

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Answer set programming is a declarative programming paradigm based on the answer set semantics of logic programs. This introductory article provides the mathematical background for the discussion of answer set programming in other contributions to this special issue.
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Brewka, Gerhard, Thomas Eiter, and Miroslaw Truszczynski. "Answer Set Programming: An Introduction to the Special Issue." AI Magazine 37, no. 3 (October 7, 2016): 5–6. http://dx.doi.org/10.1609/aimag.v37i3.2669.

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This editorial introduces answer set programming, a vibrant research area in computational knowledge representation and declarative programming. We give a brief overview of the articles that form this special issue on answer set programming and of the main topics they discuss.
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45

LOPES, NUNO P., JUAN A. NAVARRO, ANDREY RYBALCHENKO, and ATUL SINGH. "Applying Prolog to develop distributed systems." Theory and Practice of Logic Programming 10, no. 4-6 (July 2010): 691–707. http://dx.doi.org/10.1017/s1471068410000360.

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AbstractDevelopment of distributed systems is a difficult task. Declarative programming techniques hold a promising potential for effectively supporting programmer in this challenge. While Datalog-based languages have been actively explored for programming distributed systems, Prolog received relatively little attention in this application area so far. In this paper we present a Prolog-based programming system, called DAHL, for the declarative development of distributed systems. DAHL extends Prolog with an event-driven control mechanism and built-in networking procedures. Our experimental evaluation using a distributed hash-table data structure, a protocol for achieving Byzantine fault tolerance, and a distributed software model checker—all implemented in DAHL—indicates the viability of the approach.
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CALIMERI, FRANCESCO, GIOVAMBATTISTA IANNI, and FRANCESCO RICCA. "The third open answer set programming competition." Theory and Practice of Logic Programming 14, no. 1 (September 6, 2012): 117–35. http://dx.doi.org/10.1017/s1471068412000105.

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AbstractAnswer Set Programming (ASP) is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since its first informal editions, ASP systems have been compared in the now well-established ASP Competition. The Third (Open) ASP Competition, as the sequel to the ASP Competitions Series held at the University of Potsdam in Germany (2006–2007) and at the University of Leuven in Belgium in 2009, took place at the University of Calabria (Italy) in the first half of 2011. Participants competed on a pre-selected collection of benchmark problems, taken from a variety of domains as well as real world applications. The Competition ran on two tracks: the Model and Solve (M&S) Track, based on an open problem encoding, and open language, and open to any kind of system based on a declarative specification paradigm; and the System Track, run on the basis of fixed, public problem encodings, written in a standard ASP language. This paper discusses the format of the competition and the rationale behind it, then reports the results for both tracks. Comparison with the second ASP competition and state-of-the-art solutions for some of the benchmark domains is eventually discussed.
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47

Van Hentenryck, Pascal. "Constraint logic programming." Knowledge Engineering Review 6, no. 3 (September 1991): 151–94. http://dx.doi.org/10.1017/s0269888900005798.

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AbstractConstraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from their underlying theory, to their constraint systems, and to their applications to combinatorial problems.
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48

Madkour, Abdelrahman, Chris Martens, Steven Holtzen, Casper Harteveld, and Stacy Marsella. "Probabilistic Logic Programming Semantics For Procedural Content Generation." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 19, no. 1 (October 6, 2023): 295–305. http://dx.doi.org/10.1609/aiide.v19i1.27525.

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Abstract:
Research in procedural content generation (PCG) has recently heralded two major methodologies: machine learning (PCGML) and declarative programming. The former shows promise by automating the specification of quality criteria through latent patterns in data, while the latter offers significant advantages for authorial control. In this paper we propose the use of probabilistic logic as a unifying framework that combines the benefits of both methodologies. We propose a Bayesian formalization of content generators as probability distributions and show how common PCG tasks map naturally to operations on the distribution. Further, through a series of experiments with maze generation, we demonstrate how probabilistic logic semantics allows us to leverage the authorial control of declarative programming and the flexibility of learning from data.
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49

Sinyugin, N. P. "Visual declarative-algorithmic language «InteloGraf» in rocket space technology." Informacionno-technologicheskij vestnik 14, no. 4 (December 30, 2017): 156–64. http://dx.doi.org/10.21499/2409-1650-2017-4-156-164.

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50

Paramonov, A. V. "ADVANCED TOOLS OF DECLARATIVE PLC PROGRAMMING USING VISUAL MODELS." Scientific and Technical Volga region Bulletin 8, no. 5 (May 2018): 239–41. http://dx.doi.org/10.24153/2079-5920-2018-8-5-239-241.

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