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Books on the topic 'Artificial symbol learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Apolloni, Bruno. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Boston, MA: Springer US, 2002.

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2

Conference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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3

E, Diday, and Institut national de recherche en informatique et en automatique (France), eds. Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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4

Gabbay, Dov M. Abductive Reasoning and Learning. Dordrecht: Springer Netherlands, 2000.

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5

Pascal, Hitzler, and SpringerLink (Online service), eds. Perspectives of Neural-Symbolic Integration. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.

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6

International School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data (2002 Erice, Italy). From synapses to rules: Discovering symbolic rules from neural processed data. New York: Kluwer Academic/Plenum Pub., 2002.

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7

Laurent, Miclet, De la Higuera Colin, and International Colloquium on Grammatical Inference (3rd : 1996 : Montpellier, France), eds. Grammatical inference: Learning syntax from sentences : Third International Colloquium, ICGI-96, Montpellier, France, September 25-27, 1996 : proceedings. Berlin: Springer, 1996.

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8

Pieter, Adriaans, Fernau Henning 1965-, and Zaanen Menno van 1972-, eds. Grammatical inference: Algorithms and applications : 6th international colloquium, ICGI 2002, Amsterdam, The Netherlands, September 23-25, 2002 : proceedings. New York: Springer, 2002.

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9

Proudfoot, Diane, and B. Jack Copeland. Artificial Intelligence. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0007.

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In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article inMind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols.
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10

Neural-Symbolic Learning Systems. Springer, 2002.

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11

Boden, Margaret A. 4. Artificial neural networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0004.

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Artificial neural networks (ANNs) are made up of many interconnected units, each one capable of computing only one thing. ANNs have myriad applications, from playing the stock market and monitoring currency fluctuations to recognizing speech or faces. ANNs are parallel-processing virtual machines implemented on classical computers. They are intriguing partly because they are very different from the virtual machines of symbolic AI. Sequential instructions are replaced by massive parallelism, top-down control by bottom-up processing, and logic by probability. ‘Artificial neural networks’ considers the wider implications of ANNs and discusses parallel distributed processing (PDP), learning in neural networks, back-propagation, deep learning, and hybrid systems.
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12

Caelli, Terry, and Garry Briscoe. A Compendium of Machine Learning: Symbolic Machine Learning (Ablex Series in Artificial Intelligence). Ablex Publishing Corporation, 1995.

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13

Logic For Learning Learning Comprehensible Theories From Structured Data. Springer, 2010.

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14

Hammer, Barbara, and Pascal Hitzler. Perspectives of Neural-Symbolic Integration. Springer Berlin / Heidelberg, 2010.

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15

(Editor), Bruno Apolloni, and Franz Kurfess (Editor), eds. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Springer, 2002.

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16

(Editor), Jonathan Lawry, Jimi Shanahan (Editor), and Anca Ralescu (Editor), eds. Modelling with Words: Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). Springer, 2004.

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17

N, Vagin V., ed. Dostovernyĭ i pravdopodobnyĭ vyvod v intellektualʹnykh sistemakh. Moskva: Fizmatlit, 2004.

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18

(Editor), Laurent Miclet, and Colin de la Higuera (Editor), eds. Grammatical Inference: Learning Syntax from Sentences: Third International Colloquium, ICGI-96, Montpellier, France, September 25 - 27, 1996. Proceedings (Lecture Notes in Computer Science). Springer, 1996.

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