In this paper the important problem of ontology clustering is considered with the purpose of optimization of intelligent data processing in conditions of uncertainty caused by inaccuracy or incompleteness of data in the subject area. The clustering of ontologies is the process of automatic splitting of a set of ontologies into groups (clusters) based on their similarity degree.
The stochastic game model of decision making in hierarchical systems with an authoritarian style of management was constructed. An adaptive recurrent method for solving a stochastic game under a priori uncertainty is developed on the basis of stochastic
The stochastic game method of coalitions formation in multiagent systems is offered. Adaptive algorithm for stochastic game solving are developed. Computer modelling of stochastic game is executed. The parameter influences on convergence of stochastic game method for coalitions formation are studied. The analysis of received results is realized.
Game model of decision-making in hierarchical systems functioning in the conditions of aprioristic uncertainty it is constructed. The adaptive recurrent method and algorithm of stochastic game solving are developed. Computer modelling of stochastic game of decision-making in hierarchical system with structure of a binary tree is executed. Influence of parameters on convergence of a game method is investigated.
The problem of dynamic coordination of strategies of multiagent systems in the conditions of uncertainty on the basis of stochastic game model is solved. Dynamic coordination consists in system learning to generate the spatially-distributed periodic signals. The model of stochastic game is constructed, criteria of dynamic coordination of strategies are defined, a recurrent method, algorithmic and software support for the stochastic game solving are developed.
The problem of reinforcement learning of multiagent systems in the game formulation is considered. The Markovian model of stochastic game is constructed, criteria of game learning are formulated, the Q-method and corresponding algorithm of the stochastic game solving are described, results of computer realization of a Q-method are analyzed.
The adaptive game method of events synchronization in multiagent systems in the conditions of uncertainty is developed. The essence of a method consists in alignment of delays of approach of events on the basis of supervision of actions of the next players. The formulation of stochastic game is executed and game algorithm for its solving is developed. Influences of parameters on convergence of a game method are investigated by means of computer experiment.
The model of matrix stochastic game for decision-making in the conditions of uncertainty is developed. The method of Q-learning for stochastic game solving with a priori unknown gains matrices is offered. The formulation of a game problem is executed. The Markovian recurrent method and algorithm for the game solving are described. Results of computer modelling of stochastic game with Q-learning are received and analysed.