V. K. Finn, “The inductive J.S. Mill’s methods in artificial intelligence systems. Part II”, Artificial Intelligence and Decision Making, 2010, no. 4, 14–40; Scientific and Technical Information Processing, 39:5 (2012), 241

Artificial Intelligence and Decision Making

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Artificial Intelligence and Decision Making, 2010, Issue 4, Pages 14–40 (Mi iipr513)

This article is cited in 23 scientific papers (total in 23 papers)

Data mining

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

V. K. Finn

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

Full-text PDF (520 kB) Citations (23)

Abstract: The deductibility of hypotheses from Facts Bases using for discovery of different kinds of JSM-reasoning correctness is defined in the paper. The correspondence between JSM-reasoning total correctness and the tolerance spaces is established. The inductive method of residues and inductive method of concomitant variations are formalized and corresponding JSM-method strategies are defined in the paper. Dynamic regularities in Facts Bases are operationally defined by plausible inference rules for the method of concomitant variations. The features of JSM-method of automatic hypotheses generation in Intelligent Systems considered as the tool of knowledge discovery are discussed also.

Keywords: JSM-method, inductive method of residues, inductive method of concomitant variations, tolerance spaces, abductive convergence, static and dynamic regularities in Facts Bases.

English version:
Scientific and Technical Information Processing, 2012, Volume 39, Issue 5, Pages 241–260
DOI: https://doi.org/10.3103/S0147688212050036

Bibliographic databases:

Document Type: Article

Language: Russian

Citation: V. K. Finn, “The inductive J.S. Mill’s methods in artificial intelligence systems. Part II”, Artificial Intelligence and Decision Making, 2010, no. 4, 14–40; Scientific and Technical Information Processing, 39:5 (2012), 241–260

Citation in format AMSBIB

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\by V.~K.~Finn

\paper The inductive J.S. Mill’s methods in artificial intelligence systems. Part~II

\jour Artificial Intelligence and Decision Making

\yr 2010

\issue 4

\pages 14--40

\mathnet{http://mi.mathnet.ru/iipr513}

\elib{https://elibrary.ru/item.asp?id=17331421}

\transl

\jour Scientific and Technical Information Processing

\yr 2012

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\crossref{https://doi.org/10.3103/S0147688212050036}

Linking options:

https://www.mathnet.ru/eng/iipr513

https://www.mathnet.ru/eng/iipr/y2010/i4/p14

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 23 articles:

M. I. Zabezhailo, “Intelligent Data Analysis As an Evidence-Based Medicine Tool”, Autom. Doc. Math. Linguist., 58:2 (2024), 129
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, “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, 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
S. M. Gusakova, “The Structure of Relations in a Set of JSM Strategies”, Autom. Doc. Math. Linguist., 56:3 (2022), 111
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
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 Problem of AI-Tools Application in Digital Control Systems”, Autom. Doc. Math. Linguist., 56:5 (2022), 229
Sanjeev Kumar Punia, Manoj Kumar, Amit Sharma, Advances in Intelligent Systems and Computing, 1172, Intelligent Computing and Applications, 2021, 793
D. V. Vinogradov, “Lattice theory for machine learning”, 49, no. 5, 2022, 379–384
M. I. Zabezhailo, Yu. Yu. Trunin, “On the Importance of Empirical Contradiction for Reliability Estimation of Intelligent Data Analysis Results”, Autom. Doc. Math. Linguist., 55:3 (2021), 94
V. K. Finn, “Exact Epistemology and Artificial Intelligence”, Autom. Doc. Math. Linguist., 54:3 (2020), 140
V.K. Finn, V.K. Finn, “Tochnaya epistemologiya i iskusstvennyi intellekt”, Nauchno-tekhnicheskaya informatsiya. Seriya 2: Informatsionnye protsessy i sistemy, 2020, no. 6, 1
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
M. I. Zabezhailo, Yu. Yu. Trunin, “On the Problem of Medical Diagnostic Evidence: Intelligent Analysis of Empirical Data on Patients in Samples of Limited Size”, Autom. Doc. Math. Linguist., 53:6 (2019), 322
V. K. Finn, O. P. Shesternikova, “The Heuristics of Detection of Empirical Regularities by JSM Reasoning”, Autom. Doc. Math. Linguist., 52:5 (2018), 215

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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