Abstract: The article explores a set of issues determining the synergetics of artificial cognitive systems: conditions for the realization of non-equilibrium stability of systems, synthesis options of artificial cognitive system, as well as mechanisms of self-organization of consciousness formed on its basis. Artificial cognitive systems are proposed to include not only artificial intelligence systems imitating human thinking, but any multilevel systems that perform the functions of recognizing and remembering information, decision-making, storage, explanation, understanding and production of new knowledge. The defining property of a cognitive system is the ability to make decisions. It is shown that the content of the cognitive system is consciousness, interpreted within the framework of the information concept as an information environment in which the extended model of reality is realized. Consciousness can be qualified as an open dynamic system. The state of such systems is determined by the processes occurring in them (thought processes – in the case of consciousness). For such systems, called "living", there is a realization of mechanisms of stable disequilibrium. The implementation of an artificial cognitive system is technically carried out on the basis of an artificial neural network, and organizationally – according to the actor or reactor model. Self-organization processes determining the synergetics of consciousness are a special case of the extreme principle, which is a consequence of the law of excessive reaction of supersystems, regulating the existence of dissipative systems at the expense of supersystem resources. The initiation of self-organization of consciousness is carried out by the process of thinking, which, due to the non-equilibrium stability of consciousness, does not stop. Thus thinking can be interpreted as a process of varying system parameters in search of suitable ones for extended modeling of reality.

Abstract: The paper investigates the possibilities of configuring the control cycle, i.e., determining the distribution of time intervals required for the execution of individual control operations across execution threads, which ensures the realizability of control. The object of research in this article are control systems with object-oriented architecture, assuming a combined vertical-horizontal integration of functional blocks and modules that distribute all control tasks among themselves. This architecture is realized by means of an actor instrumental model using metaprogramming. Such control systems are best at reducing control cycle time by performing computational and other control operations in parallel. Several approaches to control cycle configuration are considered: without optimization, with combinatorial optimization in time, with combinatorial optimization in system resources. Also, achieving a near-optimal configuration can be achieved by using adaptive configuration. Research shows that the control system cycle configuration problem has several solutions. Practical obtaining a solution to the configuration problem in the case of combinatorial optimization is associated with significant difficulties due to the high algorithmic complexity of the problem and a large amount of required computations, rapidly growing as the number of operations at the stages of the control cycle. A possible means of overcoming these difficulties is the use of stochastic methods, which sharply reduce the required amount of computation. Also, a significant reduction in the complexity of the task of configuring the control system cycle can be achieved by using adaptive configuration, which has two variants of realization. The first variant is the real-time configuration of the control system cycle. The second variant is the determination of quasi-optimal configuration on the basis of multiple configurations with different initial data and subsequent comparison of the obtained results.

Abstract: The article is devoted to the study of the problem of increasing the reliability of modeling of cognitive systems, to which the authors refer not only human intelligence, but also artificial intelligence systems, as well as intelligent control systems for production, technological processes and complex equipment. It is shown that the use of cognitive systems for solving control problems causes very high rapidity requirements for them. These requirements combined with the necessity to simplify modeling methods as the modeling object becomes more complex determine the choice of an approach to modeling cognitive systems. Models should be based on the use of simple algorithms in the form of trend detection, correlation, as well as (for solving intellectual problems) on the use of algorithms based on the application of various patterns of forms and laws. In addition, the models should be decentralized. An adequate representation of decentralized systems formed from a large number of autonomous elements can be formed within the framework of agent-based models. For cognitive systems, two models are the most elaborated: actor and reactor models. Actor models of cognitive systems have two possible realizations: as an instrumental model or as a simulation. Both implementations have the right to exist, but the possibilities of realizing a reliable description when using the tool model are higher, because it provides incommensurably higher rapidity, and also assumes variability of the modeled reality. The actor model can be realized by means of a large number of existing programming languages. The solution to the problem of creating simulative actor models is available in most languages that work with actors. Realization of instrumental actor models requires rapidity, which is unattainable in imperative programming. In this case, the optimal solution is to use actor metaprogramming. Such programming is realizable in many existing languages.

Abstract: The article deals with the management of complex systems. The general definitions of the concepts "control" and "control system" are formulated. It is stated that the control system in its basis is an information system, for which the most important characteristics are performance and rapidity. Definitions are given and differences between these characteristics are revealed. The problem of realizability of control of complex systems is stated, which consists in the necessity of providing sufficient rapidity, at which the whole necessary complex of control operations is placed in the control cycle. The relationship between the control parameters: the complexity of the control object, the duration of the control cycle and the rapidity of the control system is investigated. As a result, a number of significant dependencies are revealed: the duration of the control cycle is approximately inversely proportional to the complexity of the control object; the rapidity of the control system is approximately proportional to the square of the object complexity. It is stated that within the framework of the general theory of systems there are two main options for increasing the stability of a complex system: the option of monocentrism with a central element, or by increasing the number of links in the object. The first option does not allow increasing rapidity. The second variant of stability can be implemented in practice in the form of a decentralized system. The latter option is universally realized in living systems and is promising for the control of technical systems.

Abstract: The article is devoted to the study of the nature of consciousness within the framework of the information concept. The paper proposes a definition of consciousness as an informational environment in which an extended model of reality is realized. The process of realization of this extended model is defined as thinking. The result of thinking is information objects that form a system in the form of information environment. Information objects are reflections of the real world properties, not directly, but by means of translation through a special object – a carrier of consciousness. In the case of human consciousness, such a carrier is a human being (represented in the form of his nervous system). As a result, consciousness can be qualified as a simulacrum of reality, i.e., a model of a model: an information model of the carrier of consciousness, which in turn is a means of physical modeling of the real "big" world. Possible mechanisms mediating thinking are considered. For this purpose, two new concepts are introduced: neural circuit and neurophysical pattern. An approach to the study of self-consciousness based on the localization of the consciousness carrier in the multidimensional space of states of initial real objects, as well as their reflections in the form of information objects is proposed. This localization is ensured by the presence of feedbacks. Summarizing the results of the study, the article states the following connection between consciousness, self-consciousness and subjectness: under certain conditions (when consciousness is localized in the state space of the carrier of consciousness), consciousness acquires the property of self-consciousness, a special case of which (when the initiator of changes determining localization is the carrier of consciousness) is self-consciousness endowed with subjectness.

Abstract: The article analyzes the possibilities and limitations of artificial intelligence. The article considers the subjectivity of artificial intelligence, determines its necessity for solving intellectual problems depending on the possibility of representing the real world as a deterministic system. Methodological limitations of artificial intelligence, which is based on the use of big data technologies, are stated. These limitations cause the impossibility of forming a holistic representation of the objects of cognition and the world as a whole. As a tool for deterministic description of the universe it is proposed to use empirical-metaphysical general theory of systems, which is an extension of existing general theories of systems due to ontological justification of the phenomenon of isomorphism and definition of a limited set of laws, rules, patterns and primitives of forms and relations of objects in the universe. The distinction of natural (human) and artificial intelligence is considered, including the realization of multisystem integration of intelligence in physical, biological, social and spiritual systems. A philosophically grounded approach to ensuring the evolutionary properties of artificial intelligence is formulated, based on the inclusion of non-equilibrium mechanisms through which stability is realized.