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Bibliografias temáticas / Induction (Logic)

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Literatura científica selecionada sobre o tema "Induction (Logic)"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 30 de julho de 2024

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Artigos de revistas sobre o assunto "Induction (Logic)"

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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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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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 log

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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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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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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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Teses / dissertações sobre o assunto "Induction (Logic)"

1

Wedin, Hanna. "Mathematical Induction." Thesis, Uppsala universitet, Algebra och geometri, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414099.

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2

Hill, Alexandra. "Reasoning by analogy in inductive logic." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/reasoning-by-analogy-in-inductive-logic(039622d8-ab3f-418f-b46c-4d4e7a9eb6c1).html.

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This thesis investigates ways of incorporating reasoning by analogy into Pure (Unary) Inductive Logic. We start with an analysis of similarity as distance, noting that this is the conception that has received most attention in the literature so far. Chapter 4 looks in some detail at the consequences of adopting Hamming Distance as our measure of similarity, which proves to be a strong requirement. Chapter 5 then examines various adaptations of Hamming Distance and proposes a subtle modification, further-away-ness, that generates a much larger class of solutions.

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Lapointe, Stéphane. "Induction of recursive logic programs." Thesis, University of Ottawa (Canada), 1992. http://hdl.handle.net/10393/7467.

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Rowan, Michael. "Hume, probability and induction." Title page, contents and abstract only, 1985. http://web4.library.adelaide.edu.au/theses/09PH/09phr877.pdf.

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Caldon, Patrick Computer Science &amp Engineering Faculty of Engineering UNSW. "Limiting programs for induction in artificial intelligence." Awarded by:University of New South Wales, 2008. http://handle.unsw.edu.au/1959.4/37484.

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This thesis examines a novel induction-based framework for logic programming. Limiting programs are logic programs distinguished by two features, in general they contain an infinite data stream over which induction will be performed, and in general it is not possible for a system to know when a solution for any program is correct. These facts are characteristic of some problems involving induction in artificial intelligence, and several problems in knowledge representation and logic programming have exactly these properties. This thesis presents a specification language for problems with an in

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6

Tappert, Peter M. "Damage identification using inductive learning." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-05092009-040651/.

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7

Kimber, Timothy. "Learning definite and normal logic programs by induction on failure." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9961.

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This thesis presents two novel inductive logic programming (ILP) approaches, based on the notion of a connected theory. A connected theory contains clauses that depend on one another, either directly or via clauses in the background knowledge. Generalisation of such a theory is proved to be a sound and complete method for learning definite ILP hypotheses. The Induction on Failure (IOF) proof procedure, based on the connected theory generalisation method, adds secondary examples into the hypothesis, and generates auxiliary clauses to explain them. These concepts, novel to IOF, address the issue

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8

Barnes, Valerie Elizabeth. "The quality of human judgment : an alternative perspective /." Thesis, Connect to this title online; UW restricted, 1985. http://hdl.handle.net/1773/9139.

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9

Syed, Altaf Ahmad. "Applied Fuzzy Logic Controls for Improving Dynamic Response of Induction Machines." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1219671348.

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10

Barker, Gillian Abernathy. "Abstraction, analogy and induction : toward a general account of ampliative inference /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9820857.

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Livros sobre o assunto "Induction (Logic)"

1

Millgram, Elijah. Practical induction. Cambridge, Mass: Harvard University Press, 1997.

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2

I, Craig, and Cohn A. G, eds. The logic of induction. Chichester [England]: Halsted Press, 1988.

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3

Gabbay, Dov M. Inductive Logic. San Diego: North Holland [Imprint], 2011.

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4

Kawalec, Paweł. Structural reliabilism: Inductive logic as a theory of justification. Dordrecht: Kluwer Academic Publishers, 2003.

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5

Baird, Davis. Inductive logic: Probability and statistics. Englewood Cliffs, N.J: Prentice Hall, 1992.

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6

Zhang, H. Automated Mathematical Induction. Dordrecht: Springer Netherlands, 1996.

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7

Williams, Donald Cary. The ground of induction. Cambridge, MA: Harvard University Press, 1991.

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8

Skyrms, Brian. Choice and chance: An introduction to inductive logic. 4th ed. Australia: Wadsworth/Thomson Learning, 2000.

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9

Skyrms, Brian. Choice and chance: An introduction to inductive logic. 3rd ed. Belmont, Calif: Wadsworth Pub. Co., 1986.

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10

A, Flach Peter, and Kakas Antonis C, eds. Abduction and induction: Essays on their relation and integration. Dordrecht: Kluwer Academic, 2000.

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Capítulos de livros sobre o assunto "Induction (Logic)"

1

Genesereth, Michael, and Eric Kao. "Induction." In Introduction to Logic, 111–21. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01798-8_9.

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2

Genesereth, Michael, and Eric Kao. "Induction." In Introduction to Logic, 121–36. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-01799-5_9.

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Genesereth, Michael, and Eric J. Kao. "Induction." In Introduction to Logic, 121–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-031-01801-5_11.

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4

Anthony, Simon, and Alan M. Frisch. "Cautious induction in inductive logic programming." In Inductive Logic Programming, 45–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3540635149_34.

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5

Kotlarski, Henryk. "Transfinite Induction." In Trends in Logic, 73–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28921-8_4.

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6

Sethy, Satya Sundar. "Induction." In Introduction to Logic and Logical Discourse, 243–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2689-0_15.

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7

Adriaans, Pieter, and Erik de Haas. "Grammar Induction as Substructural Inductive Logic Programming." In Learning Language in Logic, 127–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40030-3_8.

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8

Falke, Stephan, and Deepak Kapur. "Inductive Decidability Using Implicit Induction." In Logic for Programming, Artificial Intelligence, and Reasoning, 45–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11916277_4.

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9

Cellucci, Carlo. "Induction and Analogy." In Logic, Argumentation & Reasoning, 331–46. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6091-2_20.

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10

Otero, Ramón P. "Induction of Stable Models." In Inductive Logic Programming, 193–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44797-0_16.

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Trabalhos de conferências sobre o assunto "Induction (Logic)"

1

EELLS, ELLERY. "POPPER AND MILLER, AND INDUCTION AND DEDUCTION." In 7th and 8th Asian Logic Conferences. CO-PUBLISHED WITH SINGAPORE UNIVERSITY PRESS, 2003. http://dx.doi.org/10.1142/9789812705815_0006.

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2

Eskander, M. N. "Fuzzy logic control of saturated induction machine." In 6th International Workshop on Advanced Motion Control. Proceedings. IEEE, 2000. http://dx.doi.org/10.1109/amc.2000.862878.

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3

Baunsgaard Kristensen, Magnus, Rasmus Ejlers Mogelberg, and Andrea Vezzosi. "Greatest HITs: Higher inductive types in coinductive definitions via induction under clocks." In LICS '22: 37th Annual ACM/IEEE Symposium on Logic in Computer Science. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531130.3533359.

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4

Azgomi, Hamid Fekri, and Javad Poshtan. "Induction motor stator fault detection via fuzzy logic." In 2013 21st Iranian Conference on Electrical Engineering (ICEE). IEEE, 2013. http://dx.doi.org/10.1109/iraniancee.2013.6599711.

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5

Venkatachalam, M., and S. Thangavel. "Fuzzy logic based performance improvement of induction motor." In 2012 IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA). IEEE, 2012. http://dx.doi.org/10.1109/aicera.2012.6306749.

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6

Shetgaonkar, Satej Santosh. "Fault diagnosis in induction motor using fuzzy logic." In 2017 International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2017. http://dx.doi.org/10.1109/iccmc.2017.8282693.

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Kuzelka, Ondrej, Jesse Davis, and Steven Schockaert. "Induction of Interpretable Possibilistic Logic Theories from Relational Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/160.

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The field of statistical relational learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which makes them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode re

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8

Abdel-Rahim, Naser M. B. "Fuzzy-Logic control of unsymmetrical two-phase induction motor." In IECON 2012 - 38th Annual Conference of IEEE Industrial Electronics. IEEE, 2012. http://dx.doi.org/10.1109/iecon.2012.6388925.

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Madbouly, S. O., H. F. Soliman, H. M. Hasanien, and M. A. Badr. "Fuzzy logic control of brushless doubly fed induction generator." In 5th IET International Conference on Power Electronics, Machines and Drives (PEMD 2010). Institution of Engineering and Technology, 2010. http://dx.doi.org/10.1049/cp.2010.0085.

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Johnston, Benjamin, and Guido Governatori. "Induction of defeasible logic theories in the legal domain." In the 9th international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/1047788.1047834.

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Relatórios de organizações sobre o assunto "Induction (Logic)"

1

Lukac, Martin. Quantum Inductive Learning and Quantum Logic Synthesis. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2316.

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2

Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for nu

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3

Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for nu

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