
 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.


Actor modeling of real-time cognitive systems: ontological basis and software-mathematical implementation

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. 

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Actor modeling of real-time cognitive systems: ontological basis and software-mathematical implementation

Configuration of memory-oriented motion control system

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