scholarly journals Situational analysis model in an intelligent system based on multi-agent neurocognitive architectures

2021 ◽  
Vol 2131 (2) ◽  
pp. 022103
Author(s):  
Z Nagoev ◽  
I Pshenokova ◽  
O Nagoeva ◽  
S Kankulov
Keyword(s):  
Decision Making ◽  
Control Systems ◽  
Intelligent System ◽  
Situational Analysis ◽  
Analysis Model ◽  
Intelligent Decision Making ◽  
Intelligent Software ◽  
Intelligent Decision ◽  
Multi Agent ◽  
And Control

Abstract An approach to the development of intelligent decision-making and control systems based on the hypothesis of the organization of neural activity of the brain in the process of performing cognitive functions is proposed. This approach, based on intelligent software agents with a developed cognitive architecture, is able to provide the process of extracting knowledge from an unstructured data flow, generalizing the knowledge and learning gained, to implement effective methods of synthesizing behavior aimed at solving various problems. A multi-agent model of situational analysis based on self-organization of distributed recursive neurocognitive architectures is presented. In particular, the basic principles of situational analysis based on multi-agent neurocognitive architectures are formulated and an algorithm for the preventive synthesis of the behavior of an intelligent agent aimed at avoiding negative situations for itself is developed. The performed computational experiment showed that on the basis of training the neurocognitive architecture by forming new agents-neurons and connections between them, a complex logical function of behavior control (in particular, situational analysis) develops (forms). The results of this study can be used to create intelligent decision-making and control systems for autonomous robots and robotic systems for various purposes.

scholarly journals Game theory, learning, and control systems

2019 ◽  
Vol 7 (7) ◽  
pp. 1118-1119
Author(s):  
Jeff S Shamma
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Control Systems ◽  
Decision Makers ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
And Control

Summary Game theory is the study of interacting decision makers, whereas control systems involve the design of intelligent decision-making devices. When many control systems are interconnected, the result can be viewed through the lens of game theory. This article discusses both long standing connections between these fields as well as new connections stemming from emerging applications.

scholarly journals Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4196
Author(s):  
Fu ◽  
Wang ◽  
Wang ◽  
Shi ◽  
Yang ◽  
...  
Keyword(s):  
Decision Making ◽  
Objective Function ◽  
Real Time ◽  
Decision Making Process ◽  
Layer Model ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Multi Agent ◽  
Block Chain ◽  
Micro Grid

This paper aims to study the problems of surplus interaction, poor real-time performance, and excessive processing of information in the micro-grid scheduling and decision-making process. Firstly, the micro-grid dual-loop mobile topology structure is designed by using the method of block-chain and multi-agent fusion, realizing the real-time update of the decision-making body. Secondly, on the basis of optimizing the decision-making body, a two-layer model of intelligent decision-making under the decentralized mechanism is established. Aiming at the upper model, based on the theory of block-chain consensus mechanism, this paper proposes an improved evolutionary game algorithm. The maximum risk-benefit in the decision-making process is the objective function, which realizes the evaluation and optimization of decision tasks. For the lower layer model, based on the block-chain distributed ledger theory, this paper proposes an improved hybrid game reinforcement learning algorithm, with the maximum controllable load participation as the objective function, and realizes the optimal configuration of distributed energy in the micro-grid. This paper reveals the rules of group intelligent decision making in micro-grid under multi-task. Finally, the effectiveness of the proposed algorithm is verified by using Beijing Jin-feng Energy Internet Park data.

scholarly journals A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 794
Author(s):  
Tianjun Sun ◽  
Zhenhai Gao ◽  
Fei Gao ◽  
Tianyao Zhang ◽  
Siyan Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Decision Making ◽  
Driving Simulator ◽  
Fuzzy Classification ◽  
Intelligent Decision Making ◽  
Driving Behaviors ◽  
Intelligent Decision ◽  
Linear Neural Network ◽  
And Control

Brain-like intelligent decision-making is a prevailing trend in today’s world. However, inspired by bionics and computer science, the linear neural network has become one of the main means to realize human-like decision-making and control. This paper proposes a method for classifying drivers’ driving behaviors based on the fuzzy algorithm and establish a brain-inspired decision-making linear neural network. Firstly, different driver experimental data samples were obtained through the driving simulator. Then, an objective fuzzy classification algorithm was designed to distinguish different driving behaviors in terms of experimental data. In addition, a brain-inspired linear neural network was established to realize human-like decision-making and control. Finally, the accuracy of the proposed method was verified by training and testing. This study extracts the driving characteristics of drivers through driving simulator tests, which provides a driving behavior reference for the human-like decision-making of an intelligent vehicle.

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