Vol 45, No 6 (2018)

An asynchronous discrete model of nonsynaptic chemical interactions between neurons is proposed. The model significantly extends the previous work [1, 2] by novel concepts that make it more biologically plausible. In the model, neurons interact by emitting neurotransmitters to the shared extracellular space (ECS). We introduce the dynamics of membrane potentials that comprises two factors: the endogenous rates of change depending on the neuron’s firing type and the exogenous rates of change depending on the concentrations of neurotransmitters that the neuron is sensitive to. The neuron’s firing type is determined by the individual composition of endogenous rates. We consider three basic firing types: oscillatory, tonic, and reactive. Each firing type is essential for modeling central pattern generators—neural ensembles that generate rhythmic activity in the absence of external stimuli. Differences in endogenous rates of different neurons lead to asynchronous neural interactions and significant variability of phase durations in the activity patterns present in simple neural systems. The algorithm computing the behavior of the proposed model is provided.

It is found that the features of the voice, keystroke dynamics and pattern of a subject’s use of a computer mouse contain the following information about the psychophysiological state of the operator: normal, fatigue, intoxication, excited, and relaxed (sleepy). Voice features are the best for identifying fatigue or a sleepy state of a speaker. Keystroke dynamics, aside from these states, have features that characterize the normal state of the operator. Some features of working with a computer mouse contain information about the states of intoxication and sleepiness. This experiment on state identification was based on Bayes strategies and the neural network approach; the best result was a 5.9% error in determining the state when monitoring a subject for no more than 100 s.

The mathematical model of the formation and functioning of a team of artificial intelligence agents with BDI architecture in a qualitative semiotic (sign) environment of functioning is considered in the work. As the basis of the mathematical model of the environment of functioning, the model of the “Group of robots–Environment” multi-agent dynamic system was chosen. A method for structuring the environment of functioning in the form of a partially ordered set of embedded subspaces of the state space of a dynamical system and a method for symbolizing the classes of states by symbol names that define these classes are proposed. Such a structure is defined as a qualitative conceptual framework of the environment and is called the semiotic environment of functioning. In terms of a qualitative semiotic functioning environment, a mathematical model of an intelligent agent with a BDI architecture is proposed and the conditions for the formation and functioning of teams of intelligent agents in this environment are formulated.

The results of solving collective pursuit problems that cover strategic and tactical levels of the behavior of players are generalized. The proposed approach is based on a combination of methods of intelligent and geometric control of dynamic objects, such as unmanned aerial vehicles (AVs). Aerial vehicles use behavioral strategies in a perturbed environment implemented by the proposed rules for selecting the orientation angles and flight velocities in solving the posed problems. A structural diagram of the simulation of the pursuit process that takes into account the mathematical models of aerial vehicles and wind load is presented. Experimental studies are conducted to solve problems in a perturbed environment.

The multicriteria choice problem with a fuzzy preference relation is considered. This problem involves a set of feasible alternatives, a numerical vector criterion, and a fuzzy preference relation of a decision- maker (DM). The concepts of fuzzy vector space, a polyhedral fuzzy set, and the distance between convex fuzzy sets and cones are used. To reduce the Pareto set, ultimate possibilities of using information about the fuzzy preference relation in the form of its quanta are studied. For a sufficiently wide class of choice problems, it is proved that an originally unknown fuzzy set of nondominated elements can be arbitrarily accurately approximated using a finite set of fuzzy information quanta.