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Articoli di riviste sul tema "Induction (Logic)"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 30 luglio 2024

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1

Devesas Campos, Marco, and Marcelo Fiore. "Classical logic with Mendler induction." Journal of Logic and Computation 30, no. 1 (January 2020): 77–106. http://dx.doi.org/10.1093/logcom/exaa004.

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Abstract We investigate (co-) induction in classical logic under the propositions-as-types paradigm, considering propositional, second-order and (co-) inductive types. Specifically, we introduce an extension of the Dual Calculus with a Mendler-style (co-) iterator and show that it is strongly normalizing. We prove this using a reducibility argument.

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2

Kaminsky, Jack. "Logic, Induction, and Ontology." International Studies in Philosophy 20, no. 1 (1988): 111. http://dx.doi.org/10.5840/intstudphil198820151.

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3

Howson, Colin. "A Logic of Induction." Philosophy of Science 64, no. 2 (June 1997): 268–90. http://dx.doi.org/10.1086/392551.

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4

Terwijn, Sebastiaan A. "Probabilistic Logic and Induction." Journal of Logic and Computation 15, no. 4 (August 1, 2005): 507–15. http://dx.doi.org/10.1093/logcom/exi032.

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5

Yamaguchi, Fumihiko, and Masakazu Nakanishi. "Induction in linear logic." International Journal of Theoretical Physics 35, no. 10 (October 1996): 2107–16. http://dx.doi.org/10.1007/bf02302230.

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6

Kuznetsov, Stepan. "Action Logic is Undecidable." ACM Transactions on Computational Logic 22, no. 2 (May 15, 2021): 1–26. http://dx.doi.org/10.1145/3445810.

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Action logic is the algebraic logic (inequational theory) of residuated Kleene lattices. One of the operations of this logic is the Kleene star, which is axiomatized by an induction scheme. For a stronger system that uses an -rule instead (infinitary action logic), Buszkowski and Palka (2007) proved -completeness (thus, undecidability). Decidability of action logic itself was an open question, raised by Kozen in 1994. In this article, we show that it is undecidable, more precisely, -complete. We also prove the same undecidability results for all recursively enumerable logics between action logic and infinitary action logic, for fragments of these logics with only one of the two lattice (additive) connectives, and for action logic extended with the law of distributivity.

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7

Dogan, Hamide. "Mathematical induction: deductive logic perspective." European Journal of Science and Mathematics Education 4, no. 3 (July 15, 2016): 315–30. http://dx.doi.org/10.30935/scimath/9473.

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8

Greenland, Sander. "Probability Logic and Probabilistic Induction." Epidemiology 9, no. 3 (May 1998): 322–32. http://dx.doi.org/10.1097/00001648-199805000-00018.

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9

Arieli, Itai, and Robert J. Aumann. "The logic of backward induction." Journal of Economic Theory 159 (September 2015): 443–64. http://dx.doi.org/10.1016/j.jet.2015.07.004.

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10

K.M.MAKWANA, K. M. MAKWANA, Dr B. R. PAREKH Dr.B.R.PAREKH, and SHEETAL SHINKHEDE. "Fuzzy Logic Controller Vs Pi Controller for Induction Motor Drive." Indian Journal of Applied Research 3, no. 7 (October 1, 2011): 315–18. http://dx.doi.org/10.15373/2249555x/july2013/97.

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11

Athanassopoulos, Evangelos, and Michael Gr Voskoglou. "A Philosophical Treatise on the Connection of Scientific Reasoning with Fuzzy Logic." Mathematics 8, no. 6 (June 1, 2020): 875. http://dx.doi.org/10.3390/math8060875.

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The present article studies the connection of scientific reasoning with fuzzy logic. Induction and deduction are the two main types of human reasoning. Although deduction is the basis of the scientific method, almost all the scientific progress (with pure mathematics being probably the unique exception) has its roots to inductive reasoning. Fuzzy logic gives to the disdainful by the classical/bivalent logic induction its proper place and importance as a fundamental component of the scientific reasoning. The error of induction is transferred to deductive reasoning through its premises. Consequently, although deduction is always a valid process, it is not an infallible method. Thus, there is a need of quantifying the degree of truth not only of the inductive, but also of the deductive arguments. In the former case, probability and statistics and of course fuzzy logic in cases of imprecision are the tools available for this purpose. In the latter case, the Bayesian probabilities play a dominant role. As many specialists argue nowadays, the whole science could be viewed as a Bayesian process. A timely example, concerning the validity of the viruses’ tests, is presented, illustrating the importance of the Bayesian processes for scientific reasoning.

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12

Aehlig, Klaus. "Induction and inductive definitions in fragments of second order arithmetic." Journal of Symbolic Logic 70, no. 4 (December 2005): 1087–107. http://dx.doi.org/10.2178/jsl/1129642116.

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AbstractA fragment with the same provably recursive functions as n iterated inductive definitions is obtained by restricting second order arithmetic in the following way. The underlying language allows only up to n + 1 nested second order quantifications and those are in such a way. that no second order variable occurs free in the scope of another second order quantifier. The amount of induction on arithmetical formulae only affects the arithmetical consequences of these theories, whereas adding induction for arbitrary formulae increases the strength by one inductive definition.

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13

Kaye, Richard. "Parameter-Free Universal Induction." Zeitschrift für Mathematische Logik und Grundlagen der Mathematik 35, no. 5 (1989): 443–56. http://dx.doi.org/10.1002/malq.19890350511.

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14

Unver, H. M. "Control freaks [induction furnace logic controller]." Power Engineer 19, no. 3 (2005): 36. http://dx.doi.org/10.1049/pe:20050307.

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15

Tinkham, Nancy Lynn. "Schema induction for logic program synthesis." Artificial Intelligence 98, no. 1-2 (January 1998): 1–47. http://dx.doi.org/10.1016/s0004-3702(97)00055-6.

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16

Tiu, Alwen, and Alberto Momigliano. "Cut elimination for a logic with induction and co-induction." Journal of Applied Logic 10, no. 4 (December 2012): 330–67. http://dx.doi.org/10.1016/j.jal.2012.07.007.

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17

Wong, Man Leung, and Kwong Sak Leung. "Evolutionary Program Induction Directed by Logic Grammars." Evolutionary Computation 5, no. 2 (June 1997): 143–80. http://dx.doi.org/10.1162/evco.1997.5.2.143.

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Program induction generates a computer program that can produce the desired behavior for a given set of situations. Two of the approaches in program induction are inductive logic programming (ILP) and genetic programming (GP). Since their formalisms are so different, these two approaches cannot be integrated easily, although they share many common goals and functionalities. A unification will greatly enhance their problem-solving power. Moreover, they are restricted in the computer languages in which programs can be induced. In this paper, we present a flexible system called LOGENPRO (The LOgic grammar-based GENetic PROgramming system) that uses some of the techniques of GP and ILP. It is based on a formalism of logic grammars. The system applies logic grammars to control the evolution of programs in various programming languages and represent context-sensitive information and domain-dependent knowledge. Experiments have been performed to demonstrate that LOGENPRO can emulate GP and GP with automatically defined functions (ADFs). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. The experiments show that LOGENPRO has superior performance to that of GP and GP with ADFs when more domain-dependent knowledge is available. We have applied LOGENPRO to evolve general recursive functions for the even-n-parity problem from noisy training examples. A number of experiments have been performed to determine the impact of domain-specific knowledge and noise in training examples on the speed of learning.

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18

Wilmers, George. "Bounded existential induction." Journal of Symbolic Logic 50, no. 1 (March 1985): 72–90. http://dx.doi.org/10.2307/2273790.

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The present work may perhaps be seen as a point of convergence of two historically distinct sequences of results. One sequence of results started with the work of Tennenbaum [59] who showed that there could be no nonstandard recursive model of the system PA of first order Peano arithmetic. Shepherdson [65] on the other hand showed that the system of arithmetic with open induction was sufficiently weak to allow the construction of nonstandard recursive models. Between these two results there remained for many years a large gap occasioned by a general lack of interest in weak systems of arithmetic. However Dana Scott observed that the addition alone of a nonstandard model of PA could not be recursive, while more recently McAloon [82] improved these results by showing that even for the weaker system of arithmetic with only bounded induction, neither the addition nor the multiplication of a nonstandard model could be recursive.Another sequence of results starts with the work of Lessan [78], and independently Jensen and Ehrenfeucht [76], who showed that the structures which may be obtained as the reducts to addition of countable nonstandard models of PA are exactly the countable recursively saturated models of Presburger arithmetic. More recently, Cegielski, McAloon and the author [81] showed that the above result holds true if PA is replaced by the much weaker system of bounded induction.However in both the case of the Tennenbaum phenomenon and in that of the recursive saturation of addition the problem remained open as to how strong a system was really necessary to generate the required phenomenon. All that was clear a priori was that open induction was too weak to produce either result.

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19

Dybjer, Peter, and Anton Setzer. "Induction–recursion and initial algebras." Annals of Pure and Applied Logic 124, no. 1-3 (December 2003): 1–47. http://dx.doi.org/10.1016/s0168-0072(02)00096-9.

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20

Borrego-Díaz, Joaquín. "Algebraic combinatorics in bounded induction." Annals of Pure and Applied Logic 172, no. 2 (February 2021): 102885. http://dx.doi.org/10.1016/j.apal.2020.102885.

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21

Kotlarski, Henryk. "Bounded Induction and Satisfaction Classes." Zeitschrift für Mathematische Logik und Grundlagen der Mathematik 32, no. 31-34 (1986): 531–44. http://dx.doi.org/10.1002/malq.19860323107.

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22

Abhilash, Kesa. "Scalar Control of Induction motor using Fuzzy logic." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2331–38. http://dx.doi.org/10.22214/ijraset.2021.35503.

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The main objective of the paper is to control the speed of an induction machine with scalar control technique using Fuzzy logic controller. Here the PI controller is replaced with Fuzzy Logic controller. By using Fuzzy Logic controller there are wider range of operating conditions can be covered and easier to adapt. For Fuzzy Logic controller some rules are written which play major role to control the speed of induction motor in an effective manner. The errors are evaluated according to rules defined.

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23

Priest, Graham. "The Logic of Backwards Inductions." Economics and Philosophy 16, no. 2 (October 2000): 267–85. http://dx.doi.org/10.1017/s0266267100000250.

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Backwards induction is an intriguing form of argument. It is used in a number of different contexts. One of these is the surprise exam paradox. Another is game theory. But its use is problematic, at least sometimes. The purpose of this paper is to determine what, exactly, backwards induction is, and hence to evaluate it. Let us start by rehearsing informally some of its problematic applications.

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24

Abdalla, Turki, Haroution Hairik, and Adel Dakhil. "Direct Torque Control System for a Three Phase Induction Motor With Fuzzy Logic Based Speed Controller." Iraqi Journal for Electrical and Electronic Engineering 6, no. 2 (December 1, 2010): 131–38. http://dx.doi.org/10.37917/ijeee.6.2.8.

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This paper presents a method for improving the speed profile of a three phase induction motor in direct torque control (DTC) drive system using a proposed fuzzy logic based speed controller. A complete simulation of the conventional DTC and closed-loop for speed control of three phase induction motor was tested using well known Matlab/Simulink software package. The speed control of the induction motor is done by using the conventional proportional integral (PI) controller and the proposed fuzzy logic based controller. The proposed fuzzy logic controller has a nature of (PI) to determine the torque reference for the motor. The dynamic response has been clearly tested for both conventional and the proposed fuzzy logic based speed controllers. The simulation results showed a better dynamic performance of the induction motor when using the proposed fuzzy logic based speed controller compared with the conventional type with a fixed (PI) controller.

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25

Beshai, J. A. "Toward a Phenomenology of Trance Logic in Posttraumatic Stress Disorder." Psychological Reports 94, no. 2 (April 2004): 649–54. http://dx.doi.org/10.2466/pr0.94.2.649-654.

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Some induction procedures result in trance logic as an essential feature of hypnosis. Trance logic is a voluntary state of acceptance of suggestions without the critical evaluation that would destroy the validity of the meaningfulness of the suggestion. Induction procedures in real and simulated conditions induce a conflict between two contradictory messages in experimental hypnosis. In military induction the conflict is much more subtle involving society's need for security and its need for ethics. Such conflicts are often construed by the subject as trance logic. Trance logic provides an opportunity for therapists using the phenomenology of “presence” to deal with the objectified concepts of “avoidance,” “numbing” implicit in this kind of dysfunctional thinking in Posttraumatic Stress Disorder. An individual phenomenology of induction procedures and suggestions, which trigger trance logic, may lead to a resolution of logical fallacies and recurring painful memories. It invites a reconciliation of conflicting messages implicit in phobias and avoidance traumas. Such a phenomenological analysis of trance logic may well be a novel approach to restructure the meaning of trauma.

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26

Verma, Sony, Anil Kumar, and A. K. Gupta. "Speed Control of Induction Motor using Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (January 31, 2024): 788–97. http://dx.doi.org/10.22214/ijraset.2024.58052.

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Abstract: In this paper, speed control of induction motor using fuzzy logic controller is proposed. Speed control of induction motor takes place by, Direct torque control(DTC) method i.e. by directly controlling torque. Here we have used Voltage/Frequency speed control method of induction motor. The fuzzy logic controller (FLC) solves the problem of non linearity’s and parameter variation of induction motor. Unlike the conventional standard controllers, the proposed controller has much less computationally demanding. Direct torque control scheme of induction motor is firstly used. Then, the specified rule and their membership functions of proposed fuzzy logic system will be represented. The performance of a controller is evaluated under various operating conditions. A simplified FLC with relatively fewer rules will be implemented for perfect speed control.

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27

Simpson, Stephen G., та Rick L. Smith. "Factorization of polynomials and Σ10 induction". Annals of Pure and Applied Logic 31 (1986): 289–306. http://dx.doi.org/10.1016/0168-0072(86)90074-6.

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28

Bigorajska, Teresa. "Universal Induction and True Universal Arithmetic." Mathematical Logic Quarterly 40, no. 1 (1994): 103–5. http://dx.doi.org/10.1002/malq.19940400114.

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29

Jeřábek, Emil. "The strength of sharply bounded induction." MLQ 52, no. 6 (December 2006): 613–24. http://dx.doi.org/10.1002/malq.200610019.

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30

Schwichtenberg, Helmut. "Dialectica interpretation of well-founded induction." MLQ 54, no. 3 (June 2008): 229–39. http://dx.doi.org/10.1002/malq.200710045.

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31

ابراهيم, نعمة محمد, and حيدر عبد الزهرة رحيم. "The problem of Al-Ahsa according to Karnab (an analytical study in the book The Philosophical Foundations of Physics)." Kufa Journal of Arts 1, no. 30 (January 23, 2017): 11–38. http://dx.doi.org/10.36317/kaj/2016/v1.i30.6075.

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The functional relationship between probability and statistics is evident in the theory of induction logic, and induction has an important function in philosophy, mathematical studies, and philosophy of science studies. In Logic and Philosophy of Science, f

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32

Kelly, Kevin T. "Reichenbach, induction, and discovery." Erkenntnis 35, no. 1-3 (July 1991): 123–49. http://dx.doi.org/10.1007/bf00388283.

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33

Enayat, Ali, and Fedor Pakhomov. "Truth, disjunction, and induction." Archive for Mathematical Logic 58, no. 5-6 (February 4, 2019): 753–66. http://dx.doi.org/10.1007/s00153-018-0657-9.

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34

Talaat, Hossam, Mohamed Ezzat, and Ahmed Saleh. "Fuzzy Logic Based Induction Motor Condition Monitoring." International Conference on Electrical Engineering 9, no. 9th (May 1, 2014): 1–6. http://dx.doi.org/10.21608/iceeng.2014.30380.

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35

Eskande, Mona N. "Fuzzy Logic Control of Saturated Induction Machine." ERJ. Engineering Research Journal 24, no. 4 (October 1, 2001): 33–47. http://dx.doi.org/10.21608/erjm.2001.71131.

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36

Gazda, Maciej, and Wan Fokkink. "Modal Logic and the Approximation Induction Principle." Electronic Proceedings in Theoretical Computer Science 8 (November 17, 2009): 41–50. http://dx.doi.org/10.4204/eptcs.8.4.

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37

Sabre, Ru Michael. "Semeiotic logic or, deduction, induction, and semeiotic." Semiotica 2018, no. 222 (April 25, 2018): 81–85. http://dx.doi.org/10.1515/sem-2016-0164.

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AbstractDeduction, induction, and semeiotic relate by each being a permutation of modus ponens. As deduction providing necessary inference is the means of mathematical proof, and induction establishing sound generalizations is the basis of scientific research, so semeiotic bringing to bear informed responses to experience is the core of human understanding. Semeiotic, specifically semeiosis, is shown to be Peircean abduction in the context of signs, thus producing informed response to experience.

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38

GAZDA, MACIEJ, and WAN FOKKINK. "Modal logic and the approximation induction principle." Mathematical Structures in Computer Science 22, no. 2 (February 28, 2012): 175–201. http://dx.doi.org/10.1017/s0960129511000387.

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We prove a compactness theorem in the context of Hennessy–Milner logic and use it to derive a sufficient condition on modal characterisations for the approximation induction principle to be sound modulo the corresponding process equivalence. We show that this condition is necessary when the equivalence in question is compositional with respect to the projection operators. Furthermore, we derive different upper bounds for the constructive version of the approximation induction principle with respect to simulation and decorated trace semantics.

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39

Lamma, Evelina, Paola Mello, Michela Milano, and Fabrizio Riguzzi. "Integrating induction and abduction in logic programming." Information Sciences 116, no. 1 (May 1999): 25–54. http://dx.doi.org/10.1016/s0020-0255(98)10092-0.

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40

Uyar, Okan, and Mehmet Çunkaş. "Fuzzy logic-based induction motor protection system." Neural Computing and Applications 23, no. 1 (February 11, 2012): 31–40. http://dx.doi.org/10.1007/s00521-012-0862-0.

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41

Cantini, Andrea. "Polytime, combinatory logic and positive safe induction." Archive for Mathematical Logic 41, no. 2 (February 1, 2002): 169–89. http://dx.doi.org/10.1007/s001530100105.

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42

Ta, Quang-Trung, Ton Chanh Le, Siau-Cheng Khoo, and Wei-Ngan Chin. "Automated mutual induction proof in separation logic." Formal Aspects of Computing 31, no. 2 (October 11, 2018): 207–30. http://dx.doi.org/10.1007/s00165-018-0471-5.

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Cameron-Jones, R. Mike, and J. Ross Quinlan. "Efficient top-down induction of logic programs." ACM SIGART Bulletin 5, no. 1 (January 1994): 33–42. http://dx.doi.org/10.1145/181668.181676.

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44

DA COSTA, NEWTON C. A., and STEVEN FRENCH. "Pragmatic Truth and the Logic of Induction." British Journal for the Philosophy of Science 40, no. 3 (September 1, 1989): 333–56. http://dx.doi.org/10.1093/bjps/40.3.333.

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Erdem, Esra, and Pierre Flener. "Completing open logic programs by constructive induction." International Journal of Intelligent Systems 14, no. 10 (October 1999): 995–1019. http://dx.doi.org/10.1002/(sici)1098-111x(199910)14:10<995::aid-int4>3.0.co;2-w.

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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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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”.

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Cropper, Andrew. "Forgetting to Learn Logic Programs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3676–83. http://dx.doi.org/10.1609/aaai.v34i04.5776.

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Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks.

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Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (September 1, 2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

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Otero, Margarita. "The amalgamation property in normal open induction." Notre Dame Journal of Formal Logic 34, no. 1 (December 1992): 50–55. http://dx.doi.org/10.1305/ndjfl/1093634563.

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Stump, Aaron. "From realizability to induction via dependent intersection." Annals of Pure and Applied Logic 169, no. 7 (July 2018): 637–55. http://dx.doi.org/10.1016/j.apal.2018.03.002.

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