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Exact Epistemology and Artificial Intelligence

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Abstract

A system of concepts and principles of exact epistemology (EE) is formulated, which is understood to be the means of acquiring new knowledge through heuristics that use an ordered set of plausible reasoning strategies. The result of their application to sequences of expandable fact bases is empirical regularities that replenish the knowledge bases of intelligent systems (ISs), which are a constructive means of achieving the principles of exact epistemology, the main concept of which is the definition of theoretical intelligence. We consider the JSM method of automated research support, which uses the interaction of induction, analogy, and abduction as a constructive method of exact epistemology for acquiring new knowledge through intelligent systems. The problems of the formation and development of artificial intelligence in Russia are discussed.

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Notes

  1. Obviously, this is consistent with the intention of the authors of [2].

  2. Ampliative inferences are plausible inferences by which knowledge is generated that is not directly contained in the parcels, that is, synthetic judgments according to I. Kant [6].

  3. P. Bernays in [8] suggested that heuristics should be the subject of logic and a means of rationality.

  4. The use of ampliative inferences is a necessary condition for the functioning of productive thinking in the sense of M. Wertheimer [10].

  5. Previously, they were formulated in [12].

  6. This problem was discussed by C.S. Pier in [14].

  7. See [22a].

  8. In [23] D.G. Lahuti uses the term “subject of knowledge”

  9. The term “partner man-machine system” was proposed by Tomasz Gergei.

  10. In connection with BPEE-3, see the book of K.R. Popper [15].

  11. Recall that understanding is one of the characteristics of intelligence in cognitive psychology [17].

  12. Passive consciousness, apparently, should be characterized psychophysiologically (an example is sleep [24]).

  13. We note that machine learning formalizes and automates only two intellectual abilities—recognition and classification.

  14. This, in particular, means the determinability of a relationship of similarity of facts.

  15. The fact that the array belongs to the representation of knowledge about W1-1 means the inapplicability of AI methods.

  16. For the first time, logical connectives Jν were proposed by D.A. Bochvar for three-valued logic in [37].

  17. Inference rules by analogy can also be formulated for the W1-3 ontology ([30], Ch. 10 and 11).

  18. The idea of the connection between induction and analogy belongs to D. Herschel [39].

  19. An important idea of the dynamic consideration of knowledge in logic was considered in [46].

  20. The considered existential amplifications \(A_{j}^{\sigma }\) in \( \sim _{j}^{\sigma }\) are equivalent to the assumption of nonemptiness of the antecedent \(A_{j}^{\sigma }:\neg {\kern 1pt} (D_{{2,j}}^{\sigma } = \Lambda )\) in [16].

  21. The transformation of ideas into concepts is the implementation of the ideas of C.S. Pierce on “How to make our ideas clear” [14]. See in this regard [48].

  22. See also [48].

  23. See [16] see Table 3.

  24. The logics of empirical modalities are considered in [16].

  25. Note that FB(s, h) = FB(s, j) for 1 ≤ h, j ≤ ( s+ 1)!

  26. ERA—logic of the class “detection of empirical regularities and abduction”.

  27. Hypotheses about prediction are verified according to the direct correspondent concept of truth, and hypotheses about causes are evaluated according to the indirect correspondent concept of truth.

  28. The fruitlessness of the ASSR JSM method with respect to ER means that the set ExtER corresponding to IntER is empty.

  29. The importance of monitoring inductive inferences is noted in [54].

  30. Confirmed realizations \(A_{\chi }^{\sigma }(C{\kern 1pt} ',Q)\) are determined by Df.7-2 and abduction of the second kind.

  31. However, the idea of “research” was expressed by C.S. Peirce very psychologically as a desire to get a “true opinion” [57].

  32. Recall that the AI system is a computer system that uses known AI tools (decision trees, neural networks, genetic algorithms, findings based on precedents). AI robot is formed by an intelligent system, a sensor unit and mechatronics.

  33. From the point of view of EE and related features of DM (14)', decision tree procedures [59] and formal concepts [60] are not a means of DM, since they are based on the Aristotelian “thing – properties” ontology [58], which is typical for problems of classification. Note that the use of decision trees in computer systems belongs to the class of “AI systems”, but not to the class of intelligent systems. However, the procedures of decision trees and formal concepts can be used in the preprocessing of data preparation for DM and for the procedures of the ASSR JSM-method.

  34. DM of sociological data is a means of formalized qualitative analysis of data, the result of which is the detection of determinants of social behavior, taking into account the influence of situations on it, recognizing the rationality of opinions and solving other problems.

  35. The first version of the ASSR JSM method was called “JSM method of automatic generation of hypotheses”.

  36. Perhaps this area is fashion [67].

  37. The creator of AI D. McCarthy spoke out against the behavioral and simplified interpretation of AI problems associated with the A. Turing test [1, 2].

  38. These modes correspond to the realization of reason and mind [6].

  39. Recognition of rationality of opinions is carried out by logic of argumentation.

  40. For example, the use of expert systems in the field of fashion [67].

  41. Science is an essential aspect of culture.

  42. Intellectualization is an essential cultural phenomenon, and digitalization, that is, a civilization one. Digitalization is a necessary technological aspect of intellectualization generated by AI problems.

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CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

Funding

This work was partly supported by the Russian Foundation for Basic Research (project No. 18-29-03063).

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  1. Federal Research Center Computer Science and Control, Russian Academy of Sciences, 119333, Moscow, Russia

    V. K. Finn

  2. Russian State University for the Humanities, 125993, Moscow, Russia

    V. K. Finn

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Translated by S. Avodkova

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Finn, V.K. Exact Epistemology and Artificial Intelligence. Autom. Doc. Math. Linguist. 54, 140–173 (2020). https://doi.org/10.3103/S0005105520030073

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