We present two examples of how human-like behavior can be implemented in a model of computer player to improve its characteristics and decision-making patterns in video game. At first, we describe a reinforcement learning model, which helps to choose the best weapon depending on reward values obtained from shooting combat situations. Secondly, we consider an obstacle avoiding path planning adapted to the tactical visibility measure. We describe an implementation of a smoothing path model, which allows the use of penalties (negative rewards) for walking through ``bad'' tactical positions. We also study algorithms of path finding such as improved I-ARA* search algorithm for dynamic graph by copying human discrete decision-making model of reconsidering goals similar to Page-Rank algorithm. All the approaches demonstrate how human behavior can be modeled in applications with significant perception of intellectual agent actions.
We examine the questions of applying large pyramidal neural (intellectual neuron) networks to solve equipment object control problems. We consider the description of a system for dynamic planning of mobile robot behavior, constructed based on a network of similar elements.
The task of improving the quality of forecasting returns of financial instruments using multivariate mathematical models: regression models and neural networks was analyzed. To construct a multifactor model of returns used the assumption on the influence of market factors that have a different nature. A linear multivariable regression model was constructed using stepwise inclusion algorithm. The multilayer neural network trained using back-propagation algorithm. The quality of the neural prediction models forecast much higher quality, built with the help of a regression model.
In this paper, the main purpose is to consider applications of morphological analysis in text classifiation. Morphological analysis helps us to learn grammatical features of words, grammatical semantic and the interaction between the elements of text. We propose the neurosemantic network based on morphological analysis for learning vector representations of the text’s grammatical structures and the recursive autoencoder that consists of two parts - the fist part combines two vectors of words, the second one combines two vectors of morphology.
The article discusses development of the segmented characters classifier of the Russian alphabet and of the Arabic numerals on the basis of block neural network structures including the plurality of blocks for each individual character recognition and for the synthesis block decision.