V. K. Finn, “J.S. Mill’s inductive methods in artificial intelligence systems. Part I”, Artificial Intelligence and Decision Making, 2010, no. 3, 3–21; Scientific and Technical Information Processing, 38:6 (2011), 385

Artificial Intelligence and Decision Making

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Artificial Intelligence and Decision Making, 2010, Issue 3, Pages 3–21 (Mi iipr502)

This article is cited in 28 scientific papers (total in 28 papers)

Data mining

J.S. Mill’s inductive methods in artificial intelligence systems. Part I

V. K. Finn

All-Russian Institute for Scientific and Technical Information of Russian Academy of Sciences, Moscow

Full-text PDF (469 kB) Citations (28)

Abstract: The principles of J.S. Mill’s induction are formulated. These principles are developed and formalized for the five J.S. Mill’s inductive methods (the method of agreement, the method of difference, the joint method of agreement and difference, the method of residues, and the method of concomitant variations). The method of agreement, the method of difference, and the joint method of agreement and difference are considered in the Part I. The possible strategies of plausible reasoning realizing interaction of Mill’s induction, analogy and abduction are formulated. These strategies are considered to be cognitive reasoning (the justification is provided).

Keywords: JSM-method, induction, analogy, abduction, method of agreement, method of difference, joint method of agreement and difference, method of residues, method of concomitant variations.

English version:
Scientific and Technical Information Processing, 2011, Volume 38, Issue 6, Pages 385–402
DOI: https://doi.org/10.3103/S0147688211060037

Bibliographic databases:

Document Type: Article

Language: Russian

Citation: V. K. Finn, “J.S. Mill’s inductive methods in artificial intelligence systems. Part I”, Artificial Intelligence and Decision Making, 2010, no. 3, 3–21; Scientific and Technical Information Processing, 38:6 (2011), 385–402

Citation in format AMSBIB

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\pages 3--21

\mathnet{http://mi.mathnet.ru/iipr502}

\elib{https://elibrary.ru/item.asp?id=17311860}

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\jour Scientific and Technical Information Processing

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\crossref{https://doi.org/10.3103/S0147688211060037}

Linking options:

https://www.mathnet.ru/eng/iipr502

https://www.mathnet.ru/eng/iipr/y2010/i3/p3

Cycle of papers

J.S. Mill’s inductive methods in artificial intelligence systems. Part I
V. K. Finn
Artificial Intelligence and Decision Making, 2010:3, 3–21
The inductive J.S. Mill’s methods in artificial intelligence systems. Part II
V. K. Finn
Artificial Intelligence and Decision Making, 2010:4, 14–40

This publication is cited in the following 28 articles:

M. I. Zabezhailo, M. A. Mikheyenkova, Yu. Yu. Trunin, “On the Nonbinary Version of the Causality Relation in the Intelligent Analysis of Oncological Data”, Autom. Doc. Math. Linguist., 58:3 (2024), 200
M. I. Zabezhailo, “Intelligent Data Analysis As an Evidence-Based Medicine Tool”, Autom. Doc. Math. Linguist., 58:2 (2024), 129
S. M. Gusakova, “Analysis of Detection of Empirical Regularity in Problems with a Similarity Operation Corresponding to Global Similarity”, Autom. Doc. Math. Linguist., 58:3 (2024), 208
M. I. Zabezhailo, “On the Problem of Explaining the Results of Intelligent Data Analysis”, Pattern Recognit. Image Anal., 34:3 (2024), 498
N. A. Simonov, “Development of an Apparatus of Imaginative Information Representation for Neuromorphic Devices”, Russ Microelectron, 53:5 (2024), 423
M. I. Zabezhailo, “Three Comprehension Test Questions for Fellow Members”, Pattern Recognit. Image Anal., 33:3 (2023), 555
M. I. Zabezhailo, A. V. Amentes, “Some Features of Intelligent Analysis of Empirical Data Collections Updated with New Information, but Limited in Size”, Autom. Doc. Math. Linguist., 57:3 (2023), 172
A. Grusho, N. Grusho, M. Zabezhailo, E. Timonina, Lecture Notes in Networks and Systems, 777, Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI'23), 2023, 100
V. K. Finn, “JSM Reasoning and Knowledge Discovery: Ampliative Reasoning, Causality Recognition, and Three Kinds of Completeness#”, Autom. Doc. Math. Linguist., 56:2 (2022), 79
E. A. Efimova, “Analysis of Results of JSM Reasoning Applied to Covid-19 Testees' Data”, Autom. Doc. Math. Linguist., 56:6 (2022), 285
M. I. Zabezhailo, “On the Problem of AI-Tools Application in Digital Control Systems”, Autom. Doc. Math. Linguist., 56:5 (2022), 229
D. V. Vinogradov, “Algebraic machine learning: emphasis on efficiency”, Autom. Remote Control, 83:6 (2022), 831–846
Alexander Grusho, Nikolai Grusho, Michael Zabezhailo, Elena Timonina, Communications in Computer and Information Science, 1552, Distributed Computer and Communication Networks, 2022, 420
M. I. Zabezhailo, “On the Capacity of Families of Characteristic Functions That Ensure Diagnostic Problems Are Solved Correctly”, Sci. Tech. Inf. Proc., 49:5 (2022), 385
D. V. Vinogradov, “Lattice theory for machine learning”, 49, no. 5, 2022, 379–384
M. I. Zabezhailo, Yu. Yu. Trunin, “To the reliability of medical diagnosis based on empirical data”, 48, no. 5, 2021, 415–422
S. M. Gusakova, A. N. Okhlupina, “Intelligent DSM Systems as an Automated Support Tool for Scientific Research on Handwriting”, Autom. Doc. Math. Linguist., 53:3 (2019), 114
V. K. Finn, O. P. Shesternikova, “The Heuristics of Detection of Empirical Regularities by JSM Reasoning”, Autom. Doc. Math. Linguist., 52:5 (2018), 215
V. K. Finn, O. P. Shesternikova, “On JSM reasoning applicable to unions of factbase subsets: Part 1”, Autom. Doc. Math. Linguist., 51:5 (2017), 220
V. K. Finn, O. P. Shesternikova, “On JSM Reasoning Applicable to Unions of Factbase Subsets: Part 2”, Autom. Doc. Math. Linguist., 51:6 (2017), 266

Citing articles in Google Scholar: Russian citations, English citations
Related articles in Google Scholar: Russian articles, English articles

Artificial Intelligence and Decision Making

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