Part 1. General problems of the intellectual systems theory

Mathematical foundations of theoretical computer science (informatics). The theory of fuzzy semantic information about a point. Semantics of natural language

A. V. Chechkin<sup>ab

a</sup> Financial University under the Government of the Russian Federation, Moscow
^b Military Academy of Missiles Forces after Peter the Great, of Russia Federation

Abstract: Introduction. In cybernetics, they deal with information for managing a single object, that is, with information related to only one point, which, due to its uniqueness, does not require a unique semantic pointer. Cybernetics serves automatic control systems. Computer science(Informatics) uses information about fundamentally different points, which requires fundamentally different, autonomous, unique semantic pointers for these points. In humans, cybernetics is responsible for the unconditioned reflexes of the first signaling system, for the subconscious regulation of human life support biomechanisms. Computer science in humans is responsible for conscious cognitive behavior of humans, for conditioned (acquired) reflexes associated primarily with natural language, with learning and with the second human signaling system (with language), [1-2]. Computer science deals with intelligent systems that use information about various points. The concept of “clear semantic information about a point” was introduced and studied in [3-4]. This concept is based on the theory of filters and ultrafilters by A. Cartan and only within the framework of the theory of classical distinct sets. Whereas the concept of “fuzzy semantic information about a point” requires special formalization, which will be discussed in this article. Сonclusion. Let’s draw general semantic conclusions. Let us clarify the concept of “natural language semantics”. In philosophy, in semiotics, within the framework of the concept of the “Frege triangle”, the semantics of a word (sign) is called content (signification, meaning) and is opposed to its denotation (the referent). In theoretical computer science, the semantics of information about a point is understood as the information about a point brought by this information. Let us single out two methodological principles for describing the semantics of dictionary words-concepts. The principle of non-uniqueness of descriptions (Semantics taken from different dictionaries) and the principle of additional descriptions (Semantics of additions from different dictionaries). The terms “stable phrase or stable word-concept” emphasize only their broad, universally recognized semantics. Note that humans have two sensory signaling systems in their central nervous system. The first system is when his sensory receptors perceive signals from natural objects or natural processes, and then subconscious reactive life–sustaining behavior occurs. The second system is when his sensory receptors perceive signals from artificial symbols, language symbols, and the semantics of these language symbols are perceived as a subconscious sensory image of a point in the primary signaling system, followed by cognitive purposeful human behavior that requires the use of a variety of information and knowledge about different points (objects and processes). At the same time, the subconscious images of the first signaling system (primary sensorium) are the leading ones in the semantics of words and phrases of natural language, for which the generally recognized clear or fuzzy semantics of stable words-concepts and stable phrases of natural language is used, for example, a private relative to an expert group (explicit or imaginary). In both human signaling systems, specific neurons play a leading role - attractors (collectors) of information about only one point. For example, the so-called neurons of “My grandmother”, “My house”, etc. [1-2]. Note the important special role of the semantic pointer of a point or the unique proper name of a point (x0) in the cognitive properties of natural language. It is thanks to the dot pointer that the process of highlighting the holistic perception of an entity (some object or some process) takes place. The details are assembled into a single whole. This highlights the objective systemic structure of the natural world.

Keywords: almost complete predicting, predicting machine, prediction of superwords by a machine, criterion for predicting