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Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 8 червня 2024

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1

Cohen, Jacques. "Logic programming and constraint logic programming." ACM Computing Surveys 28, no. 1 (March 1996): 257–59. http://dx.doi.org/10.1145/234313.234416.

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2

KOWALSKI, ROBERT, and FARIBA SADRI. "Programming in logic without logic programming." Theory and Practice of Logic Programming 16, no. 3 (March 16, 2016): 269–95. http://dx.doi.org/10.1017/s1471068416000041.

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AbstractIn previous work, we proposed a logic-based framework in which computation is the execution of actions in an attempt to make reactive rules of the form if antecedent then consequent true in a canonical model of a logic program determined by an initial state, sequence of events, and the resulting sequence of subsequent states. In this model-theoretic semantics, reactive rules are the driving force, and logic programs play only a supporting role. In the canonical model, states, actions, and other events are represented with timestamps. But in the operational semantics (OS), for the sake of efficiency, timestamps are omitted and only the current state is maintained. State transitions are performed reactively by executing actions to make the consequents of rules true whenever the antecedents become true. This OS is sound, but incomplete. It cannot make reactive rules true by preventing their antecedents from becoming true, or by proactively making their consequents true before their antecedents become true. In this paper, we characterize the notion of reactive model, and prove that the OS can generate all and only such models. In order to focus on the main issues, we omit the logic programming component of the framework.

3

Oliveira, Kleidson Êglicio Carvalho da Silva. "Paraconsistent Logic Programming in Three and Four-Valued Logics." Bulletin of Symbolic Logic 28, no. 2 (June 2022): 260. http://dx.doi.org/10.1017/bsl.2021.34.

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AbstractFrom the interaction among areas such as Computer Science, Formal Logic, and Automated Deduction arises an important new subject called Logic Programming. This has been used continuously in the theoretical study and practical applications in various fields of Artificial Intelligence. After the emergence of a wide variety of non-classical logics and the understanding of the limitations presented by first-order classical logic, it became necessary to consider logic programming based on other types of reasoning in addition to classical reasoning. A type of reasoning that has been well studied is the paraconsistent, that is, the reasoning that tolerates contradictions. However, although there are many paraconsistent logics with different types of semantics, their application to logic programming is more delicate than it first appears, requiring an in-depth study of what can or cannot be transferred directly from classical first-order logic to other types of logic.Based on studies of Tarcisio Rodrigues on the foundations of Paraconsistent Logic Programming (2010) for some Logics of Formal Inconsistency (LFIs), this thesis intends to resume the research of Rodrigues and place it in the specific context of LFIs with three- and four-valued semantics. This kind of logics are interesting from the computational point of view, as presented by Luiz Silvestrini in his Ph.D. thesis entitled “A new approach to the concept of quase-truth” (2011), and by Marcelo Coniglio and Martín Figallo in the article “Hilbert-style presentations of two logics associated to tetravalent modal algebras” [Studia Logica (2012)]. Based on original techniques, this study aims to define well-founded systems of paraconsistent logic programming based on well-known logics, in contrast to the ad hoc approaches to this question found in the literature.Abstract prepared by Kleidson Êglicio Carvalho da Silva Oliveira.E-mail: kecso10@yahoo.com.brURL: http://repositorio.unicamp.br/jspui/handle/REPOSIP/322632

4

K, Kwon. "Exception Handling in Logic Programming." Advances in Robotic Technology 1, no. 1 (October 2, 2023): 1–3. http://dx.doi.org/10.23880/art-16000104.

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One problem on logic programming is to express exception handling. We argue that this problem can be solved by adopting linear logic and prioritized-choice disjunctive goal formulas (PCD) of the form G G 0 *1 ⊕ where G0, G1 are goals. These goals have the following intended semantics: sequentially choose the first true goal GI and execute GI where i (= 0 or 1), discarding the rest if any.

5

Robinson, J. A. "Logic and logic programming." Communications of the ACM 35, no. 3 (March 1992): 40–65. http://dx.doi.org/10.1145/131295.131296.

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6

Voronkov, A. A. "Logic programming and ?-programming." Cybernetics 25, no. 1 (1989): 83–91. http://dx.doi.org/10.1007/bf01074888.

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7

Genesereth, Michael R., and Matthew L. Ginsberg. "Logic programming." Communications of the ACM 28, no. 9 (September 1985): 933–41. http://dx.doi.org/10.1145/4284.4287.

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8

Brady, Michael. "Logic Programming." Irish Journal of Psychology 10, no. 2 (January 1989): 304–16. http://dx.doi.org/10.1080/03033910.1989.10557749.

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9

Ashbacher, Charles. "From logic to logic programming." Journal of Automated Reasoning 16, no. 3 (June 1996): 427. http://dx.doi.org/10.1007/bf00252183.

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10

ANTONIOU, GRIGORIS. "LOGIC PROGRAMMING AND DEFAULT LOGIC." International Journal on Artificial Intelligence Tools 03, no. 03 (September 1994): 367–73. http://dx.doi.org/10.1142/s0218213094000194.

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We present several ideas of increasing complexity how to translate default theories to normal logic programs that make direct use of the deductive capacity of logic programming. We show the limitations of simple, ad hoc approaches, and arrive at a more general construction; its main property is that the answer substitutions computed by the logic program via its standard operational semantics correspond exactly to the extensions of the default theory.

11

Shepherdson, J. C. "From Logic to Logic Programming." Computer Journal 38, no. 1 (January 1, 1995): 78. http://dx.doi.org/10.1093/comjnl/38.1.78.

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12

Van Benthem, Johan. "Logic as Programming." Fundamenta Informaticae 17, no. 4 (November 1, 1992): 285–317. http://dx.doi.org/10.3233/fi-1992-17402.

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Starting from a general dynamic analysis of reasoning and programming, we develop two main dynamic perspectives upon logic programming. First, the standard fixed point semantics for Horn clause programs naturally supports imperative programming styles. Next, we provide axiomatizations for Prolog-type inference engines using calculi of sequents employing modified versions of standard structural rules such as monotonicity or permutation. Finally, we discuss the implications of all this for a broader enterprise of ‘abstract proof theory’.

13

Subrahmanian, V. S. "Nonmonotonic logic programming." IEEE Transactions on Knowledge and Data Engineering 11, no. 1 (1999): 143–52. http://dx.doi.org/10.1109/69.755623.

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14

Clark, K. L. "Parallel Logic Programming." Computer Journal 33, no. 6 (June 1, 1990): 482–93. http://dx.doi.org/10.1093/comjnl/33.6.482.

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15

Antoy, Sergio, and Michael Hanus. "Functional logic programming." Communications of the ACM 53, no. 4 (April 2010): 74–85. http://dx.doi.org/10.1145/1721654.1721675.

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16

Cheney, James, and Christian Urban. "Nominal logic programming." ACM Transactions on Programming Languages and Systems 30, no. 5 (August 2008): 1–47. http://dx.doi.org/10.1145/1387673.1387675.

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17

Brogi, Antonio, and Roberto Gorrieri. "Distributed Logic Programming." Journal of Logic Programming 15, no. 4 (April 1993): 295–335. http://dx.doi.org/10.1016/s0743-1066(14)80002-2.

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18

Abadi, Martín, and Zohar Manna. "Temporal logic programming." Journal of Symbolic Computation 8, no. 3 (September 1989): 277–95. http://dx.doi.org/10.1016/s0747-7171(89)80070-7.

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19

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.

20

Vojtáš, Peter. "Fuzzy logic programming." Fuzzy Sets and Systems 124, no. 3 (December 2001): 361–70. http://dx.doi.org/10.1016/s0165-0114(01)00106-3.

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21

Ebrahim, Rafee. "Fuzzy logic programming." Fuzzy Sets and Systems 117, no. 2 (January 2001): 215–30. http://dx.doi.org/10.1016/s0165-0114(98)00300-5.

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22

Muggleton, Stephen. "Inductive logic programming." New Generation Computing 8, no. 4 (February 1991): 295–318. http://dx.doi.org/10.1007/bf03037089.

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23

Bonatti, Piero A. "Autoepistemic logic programming." Journal of Automated Reasoning 13, no. 1 (1994): 35–67. http://dx.doi.org/10.1007/bf00881911.

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24

KAKAS, A. C., R. A. KOWALSKI, and F. TONI. "Abductive Logic Programming." Journal of Logic and Computation 2, no. 6 (1992): 719–70. http://dx.doi.org/10.1093/logcom/2.6.719.

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25

Laenens, Els, Domenico Sacca, and Dirk Vermeir. "Extending logic programming." ACM SIGMOD Record 19, no. 2 (May 1990): 184–93. http://dx.doi.org/10.1145/93605.98728.

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26

Pau, L. F., and H. Olason. "Visual logic programming." Journal of Visual Languages & Computing 2, no. 1 (March 1991): 3–15. http://dx.doi.org/10.1016/s1045-926x(05)80049-7.

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27

Nguyen, Linh Anh. "Multimodal logic programming." Theoretical Computer Science 360, no. 1-3 (August 2006): 247–88. http://dx.doi.org/10.1016/j.tcs.2006.03.026.

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28

Muggleton, Stephen. "Inductive logic programming." ACM SIGART Bulletin 5, no. 1 (January 1994): 5–11. http://dx.doi.org/10.1145/181668.181671.

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29

Brogi, Antonio, Paolo Mancarella, Dino Pedreschi, and Franco Turini. "Modular logic programming." ACM Transactions on Programming Languages and Systems 16, no. 4 (July 1994): 1361–98. http://dx.doi.org/10.1145/183432.183528.

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30

Bruynooghe, Maurice, and Victor Marek. "Logic programming revisited." ACM Transactions on Computational Logic 2, no. 4 (October 2001): 623–54. http://dx.doi.org/10.1145/383779.383789.

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31

Vardi, MosheY. "Database logic programming." Journal of Logic Programming 10, no. 3-4 (April 1991): 179–80. http://dx.doi.org/10.1016/0743-1066(91)90035-n.

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32

Ng, Raymond, and V. S. Subrahmanian. "Probabilistic logic programming." Information and Computation 101, no. 2 (December 1992): 150–201. http://dx.doi.org/10.1016/0890-5401(92)90061-j.

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33

Tyugu, Enn. "Inductive Logic Programming." Knowledge-Based Systems 7, no. 2 (June 1994): 149–50. http://dx.doi.org/10.1016/0950-7051(94)90030-2.

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34

Baldwin, J. F. "Support logic programming." International Journal of Intelligent Systems 1, no. 2 (1986): 73–104. http://dx.doi.org/10.1002/int.4550010202.

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35

Bollen, A. W. "Relevant logic programming." Journal of Automated Reasoning 7, no. 4 (December 1991): 563–85. http://dx.doi.org/10.1007/bf01880329.

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36

Blair, Howard A., and V. S. Subrahmanian. "Paraconsistent logic programming." Theoretical Computer Science 68, no. 2 (October 1989): 135–54. http://dx.doi.org/10.1016/0304-3975(89)90126-6.

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37

Díaz, Jaime, José Luis Carballido, and Mauricio Osorio. "Béziau’s SP3A Logic and Logic Programming." Research in Computing Science 148, no. 3 (December 31, 2019): 309–20. http://dx.doi.org/10.13053/rcs-148-3-26.

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38

ANTONIOU, GRIGORIS, DAVID BILLINGTON, GUIDO GOVERNATORI, and MICHAEL J. MAHER. "Embedding defeasible logic into logic programming." Theory and Practice of Logic Programming 6, no. 06 (October 16, 2006): 703–35. http://dx.doi.org/10.1017/s1471068406002778.

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39

Schlipf, John S. "Formalizing a logic for logic programming." Annals of Mathematics and Artificial Intelligence 5, no. 2-4 (June 1992): 279–302. http://dx.doi.org/10.1007/bf01543479.

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40

OSORIO, MAURICIO, JUAN A. NAVARRO, and JOSÉ ARRAZOLA. "Applications of intuitionistic logic in Answer Set Programming." Theory and Practice of Logic Programming 4, no. 3 (April 16, 2004): 325–54. http://dx.doi.org/10.1017/s1471068403001881.

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We present some applications of intermediate logics in the field of Answer Set Programming (ASP). A brief, but comprehensive introduction to the answer set semantics, intuitionistic and other intermediate logics is given. Some equivalence notions and their applications are discussed. Some results on intermediate logics are shown, and applied later to prove properties of answer sets. A characterization of answer sets for logic programs with nested expressions is provided in terms of intuitionistic provability, generalizing a recent result given by Pearce. It is known that the answer set semantics for logic programs with nested expressions may select non-minimal models. Minimal models can be very important in some applications, therefore we studied them; in particular we obtain a characterization, in terms of intuitionistic logic, of answer sets which are also minimal models. We show that the logic G3 characterizes the notion of strong equivalence between programs under the semantic induced by these models. Finally we discuss possible applications and consequences of our results. They clearly state interesting links between ASP and intermediate logics, which might bring research in these two areas together.

41

Balbiani, Philippe. "A Modal Semantics of Negation in Logic Programming." Fundamenta Informaticae 16, no. 3-4 (May 1, 1992): 231–62. http://dx.doi.org/10.3233/fi-1992-163-403.

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The beauty of modal logics and their interest lie in their ability to represent such different intensional concepts as knowledge, time, obligation, provability in arithmetic, … according to the properties satisfied by the accessibility relations of their Kripke models (transitivity, reflexivity, symmetry, well-foundedness, …). The purpose of this paper is to study the ability of modal logics to represent the concepts of provability and unprovability in logic programming. The use of modal logic to study the semantics of logic programming with negation is defended with the help of a modal completion formula. This formula is a modal translation of Clack’s formula. It gives soundness and completeness proofs for the negation as failure rule. It offers a formal characterization of unprovability in logic programs. It characterizes as well its stratified semantics.

42

Sakama, Chiaki, and Katsumi Inoue. "Abductive logic programming and disjunctive logic programming: their relationship and transferability." Journal of Logic Programming 44, no. 1-3 (July 2000): 75–100. http://dx.doi.org/10.1016/s0743-1066(99)00073-4.

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43

Williams, H. P. "Logic applied to integer programming and integer programming applied to logic." European Journal of Operational Research 81, no. 3 (March 1995): 605–16. http://dx.doi.org/10.1016/0377-2217(93)e0359-6.

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44

HUET, GÉRARD. "Special issue on ‘Logical frameworks and metalanguages’." Journal of Functional Programming 13, no. 2 (March 2003): 257–60. http://dx.doi.org/10.1017/s0956796802004549.

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Анотація:

There is both a great unity and a great diversity in presentations of logic. The diversity is staggering indeed – propositional logic, first-order logic, higher-order logic belong to one classification; linear logic, intuitionistic logic, classical logic, modal and temporal logics belong to another one. Logical deduction may be presented as a Hilbert style of combinators, as a natural deduction system, as sequent calculus, as proof nets of one variety or other, etc. Logic, originally a field of philosophy, turned into algebra with Boole, and more generally into meta-mathematics with Frege and Heyting. Professional logicians such as Gödel and later Tarski studied mathematical models, consistency and completeness, computability and complexity issues, set theory and foundations, etc. Logic became a very technical area of mathematical research in the last half century, with fine-grained analysis of expressiveness of subtheories of arithmetic or set theory, detailed analysis of well-foundedness through ordinal notations, logical complexity, etc. Meanwhile, computer modelling developed a need for concrete uses of logic, first for the design of computer circuits, then more widely for increasing the reliability of sofware through the use of formal specifications and proofs of correctness of computer programs. This gave rise to more exotic logics, such as dynamic logic, Hoare-style logic of axiomatic semantics, logics of partial values (such as Scott's denotational semantics and Plotkin's domain theory) or of partial terms (such as Feferman's free logic), etc. The first actual attempts at mechanisation of logical reasoning through the resolution principle (automated theorem proving) had been disappointing, but their shortcomings gave rise to a considerable body of research, developing detailed knowledge about equational reasoning through canonical simplification (rewriting theory) and proofs by induction (following Boyer and Moore successful integration of primitive recursive arithmetic within the LISP programming language). The special case of Horn clauses gave rise to a new paradigm of non-deterministic programming, called Logic Programming, developing later into Constraint Programming, blurring further the scope of logic. In order to study knowledge acquisition, researchers in artificial intelligence and computational linguistics studied exotic versions of modal logics such as Montague intentional logic, epistemic logic, dynamic logic or hybrid logic. Some others tried to capture common sense, and modeled the revision of beliefs with so-called non-monotonic logics. For the careful crafstmen of mathematical logic, this was the final outrage, and Girard gave his anathema to such “montres à moutardes”.

45

MARCOPOULOS, ELIAS, and YUANLIN ZHANG. "onlineSPARC: A Programming Environment for Answer Set Programming." Theory and Practice of Logic Programming 19, no. 2 (November 14, 2018): 262–89. http://dx.doi.org/10.1017/s1471068418000509.

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AbstractRecent progress in logic programming (e.g. the development of the answer set programming (ASP) paradigm) has made it possible to teach it to general undergraduate and even middle/high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing, and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed onlineSPARC, an online ASP environment with a self-contained file system and a simple interface. It allows users to type/edit logic programs and perform several tasks over programs, including asking a query to a program, getting the answer sets of a program, and producing a drawing/animation based on the answer sets of a program.

46

Vauzeilles, J., and A. Strauss. "Intuitionistic three-valued logic and logic programming." RAIRO - Theoretical Informatics and Applications 25, no. 6 (1991): 557–87. http://dx.doi.org/10.1051/ita/1991250605571.

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47

Genito, Daniele, Giangiacomo Gerla, and Alessandro Vignes. "Meta-logic programming for a synonymy logic." Soft Computing 14, no. 3 (February 27, 2009): 299–311. http://dx.doi.org/10.1007/s00500-009-0404-6.

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48

LEUSCHEL, MICHAEL, and TOM SCHRIJVERS. "Introduction to the 30th International Conference on Logic Programming Special Issue." Theory and Practice of Logic Programming 14, no. 4-5 (July 2014): 401–14. http://dx.doi.org/10.1017/s1471068414000581.

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Анотація:

The 30th edition of the International Conference of Logic Programming took place in Vienna in July 2014 at the Vienna Summer of Logic - the largest scientific conference in the history of logic. Following the initiative in 2010 taken by the Association for Logic Programming and Cambridge University Press, the full papers accepted for the International Conference on Logic Programming again appear as a special issue of Theory and Practice of Logic Programming (TPLP) - the 30th International Conference on Logic Programming Special Issue. Papers describing original, previously unpublished research and not simultaneously submitted for publication elsewhere were solicited in all areas of logic programming including but not restricted to: Theory: Semantic Foundations, Formalisms, Non- monotonic Reasoning, Knowledge Representation; Implementation: Compilation, Memory Management, Virtual Machines, Parallelism; Environments: Program Analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing; Language Issues: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Programming Techniques; Related Paradigms: Abductive Logic Programming, Inductive Logic Programming, Constraint Logic Programming, Answer-Set Programming; Applications: Databases, Data Integration and Federation, Software Engineering, Natural Language Processing, Web and Semantic Web, Agents, Artificial Intelligence, Bioinformatics.

49

Glasgow, J. I., M. A. Jenkins, E. Blevis, and M. P. Feret. "Logic programming with arrays." IEEE Transactions on Knowledge and Data Engineering 3, no. 3 (1991): 307–19. http://dx.doi.org/10.1109/69.91061.

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50

Nguyen, Linh Anh. "Modal logic programming revisited." Journal of Applied Non-Classical Logics 19, no. 2 (January 2009): 167–81. http://dx.doi.org/10.3166/jancl.19.167-181.

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