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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Morik, Katharina, ed. Knowledge Representation and Organization in Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/bfb0017213.

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2

Katharina, Morik, ed. Knowledge representation and organization in machine learning. Berlin: Springer-Verlag, 1989.

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3

Kumar, Avadhesh, Shrddha Sagar, T. Ganesh Kumar, and K. Sampath Kumar. Prediction and Analysis for Knowledge Representation and Machine Learning. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898.

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4

Machine learning of robot assembly plans. Boston: Kluwer Academic Publishers, 1988.

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5

Emde, Werner. Modellbildung, Wissensrevision und Wissensrepräsentation im Maschinellen Lernen. Berlin: Springer, 1991.

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6

Pacific, Rim International Conference on Artificial Intelligence (4th 1996 Cairns Qld ). PRICAI '96: Topics in artificial intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : proceedings. Berlin: Springer, 1996.

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7

1953-, Benjamin D. Paul, ed. Change of representation and inductive bias. Boston: Kluwer Academic, 1990.

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8

Motta, E. Reusable components for knowledge modelling: Case studies in parametric design problem solving. Amsterdam: IOS Press, 2000.

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9

G, Antoniou, Ghose Aditya K, Truszczyński Mirosław, Workshop on Inducing Complex Representations (1996 : Cairns, Qld.), and Pacific Rim International Conference on Artificial Intelligence (4th : 1996 : Cairns, Qld.), eds. Learning and reasoning with complex representations: PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations, Cairns, Australia, August 26-30, 1996 : selected papers. Berlin: Springer, 1998.

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10

International Conference on Knowledge Modeling & Expertise Transfer (1st 1991 Sophia-Antipolis, France). Knowledge modeling & expertise transfer: Proceedings of the first International Conference on Knowledge Modeling & Expertise Transfer, Sophia-Antipolis, French Riviera, France, April 22-24, 1991. Amsterdam: IOS Press, 1991.

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11

International Conference on Knowledge and Systems Engineering (2nd 2010 Hanoi, Vietnam). KSE 2010: Proceedings : Second International Conference on Knowledge and Systems Engineering : Hanoi, Vietnam, 7-9 October 2010. Edited by Pham Son Bao and Đại học quó̂c gia Hà Nội. University of Engineering and Technology. Los Alamitos, Calif: IEEE Computer Society, 2010.

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12

Riaño, David. Knowledge Representation for Health-Care: ECAI 2010 Workshop KR4HC 2010, Lisbon, Portugal, August 17, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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13

David, Riaño, and European Society for Artificial Intelligence in Medicine, eds. Knowledge representation for health-care: Data, processes and guidelines : AIME 2009 workshop KR4HC 2009, Verona, Italy, July 19, 2009 : revised selected papers. Berlin: Springer, 2010.

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14

Morik, Katharina. Knowledge Representation and Organization in Machine Learning. Springer, 1989.

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15

Kumar, Avadhesh, Shrddha Sagar, T. Ganesh Kumar, and K. Sampath Kumar. Prediction and Analysis for Knowledge Representation and Machine Learning. Taylor & Francis Group, 2021.

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16

Kumar, Avadhesh, Shrddha Sagar, T. Ganesh Kumar, and K. Sampath Kumar. Prediction and Analysis for Knowledge Representation and Machine Learning. Taylor & Francis Group, 2021.

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17

Kumar, Avadhesh, Shrddha Sagar, T. Ganesh Kumar, and K. Sampath Kumar. Prediction and Analysis for Knowledge Representation and Machine Learning. Taylor & Francis Group, 2021.

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18

Prediction and Analysis for Knowledge Representation and Machine Learning. Taylor & Francis Group, 2021.

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19

(Editor), Norman Foo, and Randy Goebel (Editor), eds. Pricai '96: Topics in Artificial Intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : Proce (Lecture Notes in Computer Science). Springer, 1996.

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20

Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described by a set of features. These techniques can be applied to learn knowledge required for a variety of problems in computational linguistics ranging from part-of-speech tagging and syntactic parsing to word-sense disambiguation and anaphora resolution. Finally, this article reviews the applications to a variety of these problems, such as morphology, part-of-speech tagging, word-sense disambiguation, syntactic parsing, semantic parsing, information extraction, and anaphora resolution.

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21

Change of Representation and Inductive Bias. Springer, 2011.

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22

Springer. Knowledge and Systems Engineering. Springer London, Limited, 2013.

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23

Di Maio, Paolad. Knowledge Representation for Debiasing. MTS Press, 2021. http://dx.doi.org/10.52844/slkrns1.

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Bias in Artificial Intelligence and Machine Learning is a top concern for developers and users of intelligent technology. Intelligent systems can, in theory, be designed to identify and resolve algorithmic bias, but for this to be possible it is necessary to have an adequate knowledge representation of bias itself, which has never been fully achieved. Here a KR of bias is presented as a matrix and a relational set constituting the logical and functional foundation of an intelligent system capable of autonomously identifying, measuring and minimise algorithmic bias.

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24

Domingos, Pedro. Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, 2015.

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25

Domingos, Pedro. Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Penguin Books, Limited, 2015.

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26

Domingos, Pedro. Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Penguin Books, Limited, 2017.

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27

The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books, 2015.

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28

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, 2018.

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29

United States. National Aeronautics and Space Administration, ed. Research on knowledge representation, machine learning, and knowledge acquisition: Final report covering the period 10/1/83 - 1/31/87, NASA grant number NCC 2-274. [Washington, DC: National Aeronautics and Space Administration, 1987.

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30

Pedro, Domingos, and Gorokhov Vasiliĭ translator, eds. Verkhovnyĭ algoritm: Kak mashinnoe obuchenie izmenit nash mir. 2016.

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31

L'algoritmo definitivo: La macchina che impara da sola e il futuro del nostro mondo. Bollati Boringhieri, 2016.

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32

Leondes, Cornelius T. Knowledge-Based Systems Techniques and Applications (4-Volume Set). Academic Press, 2000.

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33

The Expected Knowledge: What can we know about anything and everything? Tiruchirappalli: Sivashanmugam Palaniappan, 2012.

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34

Boden, Margaret A. 2. General intelligence as the Holy Grail. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0002.

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A host of state-of-the-art AI applications exist, designed for countless specific tasks and used in almost every area of life, by laymen and professionals alike. Many outperform even the most expert humans. In that sense, progress has been spectacular. But the AI pioneers were also hoping for systems with general intelligence. ‘General intelligence as the Holy Grail’ explains why artificial general intelligence is still highly elusive despite recent increases in computer power. It considers the general AI strategies in recent research—heuristics, planning, mathematical simplification, and different forms of knowledge representation—and discusses the concepts of the frame problem, agents and distributed cognition, machine learning, and generalist systems.

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