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Automatic Documentation and Mathematical Linguistics
Published in: Automatic Documentation and Mathematical Linguistics 5/2019

01-09-2019 | THE JSM METHOD OF AUTOMATED RESEARCH SUPPORT AND ITS APPLICATION IN INTELLIGENT SYSTEMS FOR MEDICINE

On the Heuristics of JSM Research (Additions to Articles)

Author: V. K. Finn

Published in: Automatic Documentation and Mathematical Linguistics | Issue 5/2019

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Abstract

The logical means of detecting empirical regularities using the JSM method of automated research support are considered. Generators of hypotheses about the causes and hypotheses about predictions that are stored in sequences of expandable fact bases are determined. Many “histories of possible worlds” are considered, where “world” refers to an expandable fact base. This set is used to determine empirical regularities, that is, empirical laws, tendencies, and weak tendencies. Empirical regularities are used to determine empirical modalities of necessity (for empirical laws), possibilities (for empirical tendencies), and weak possibilities (for weak empirical tendencies). The Propositional calculi of the class ERA are proposed, that is, modal logics with two empirical modalities of necessity and possibility such that they imitate abductive inference through the axioms of abduction (◻(p → q) & Tq) → ◻p), (◇(p → q) & Tq) → ◇p), where ◻, ◇, T are operators of necessity, possibility, and truth (“it is true that…”). A series of definitions related to the characterization of data mining using heuristics of the JSM method of automated research support is given.
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Appendix
Available only for authorised users
Footnotes
1
D.V. Vinogradov in [9] established that, for finite models, JSM rules are expressible in the predicate logic of the first order.
 
2
⇌ is equality by definition.
 
3
We note that there are many attempts to formalize the ideas of C.S. Peirce on abduction by means of logic and programming using deduction [20‐22].
 
4
\({{\bar {\rho }}^{\sigma }} \leqslant 1\), in recognition problems often get \({{\bar {\rho }}^{\sigma }}\) = 0.8.
 
5
We can assume that CCA(σ) is the principle of induction (J.S. Mill in [14] considered the law of uniformity of nature to be such).
 
6
(τ, 1) and (τ, 2) are sets of truth values.
 
7
According to the terminology of I. Kant in “Critique of Pure Reason” [32], ICF are the conditions of “possible experience”.
 
8
In [3], Int and Ext were considered for the initial predicates of the JSM method and the plausible inference rules.
 
9
For simplicity, we will use the number i instead of \(HP{{W}_{i}}\).
 
10
In [35], a description is given of an intelligent system that implements the ASSR JSM method for gastroenterology data. This computer system has 16 JSM strategies.
 
11
Conditions a and ad0 formalize inductive canons of similarity and difference [14]. The canons of similarity-differences are formalized in [13, 36].
 
12
An interpretation of [15] is available in [18], where the condition of the best explanation is added.
 
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Metadata
Title
On the Heuristics of JSM Research (Additions to Articles)
Author
V. K. Finn
Publication date
01-09-2019
Publisher
Pleiades Publishing
Published in
Automatic Documentation and Mathematical Linguistics / Issue 5/2019
Print ISSN: 0005-1055
Electronic ISSN: 1934-8371
DOI
https://doi.org/10.3103/S0005105519050078

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