scholarly journals DEVELOPMENT OF A MULTI-AGENT SYSTEM FOR THE ASSESSMENT OF ECOLOGICAL INFLUENCES OF TRANSPORT MEGAPROJECTS: TAKING INTO ACCOUNT THE INFORMATION ENVIRONMENT FACTOR

2020 ◽  
Vol 3 (1) ◽  
pp. 61-67
Author(s):  
Tatyana N. Yesikova ◽  
Svetlana V. Vakhrusheva
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
A Priori ◽  
Dynamic Environment ◽  
Information Environment ◽  
Information Space ◽  
Bering Strait ◽  
Multi Agent Systems ◽  
Multi Agent ◽  
Business Entities

The paper considers the issues of accounting and reflection in multi-agent systems of the influence of the information environment, information flows on agent behavior and the assessment of consequences, including environmental ones, of decisions made by them at various stages of large-scale infrastructure projects. The information space is a priori a multidimensional dynamic environment that is continuously updated and transformed, sometimes under the primacy of the interests of individual agents or influence groups, and much less frequently from the standpoint of ensuring the viability of the economic system as a whole. A large-scale project for the construction of a transcontinental highway (TKS) through the Bering Strait was chosen as the object of study. The article provides a fairly detailed description of the groups of agents involved in the decision-making process, as well as the elements of the information space that are significant for an agent at certain stages of its activity. To model the influence of the information space on decision-making processes by agents of different hierarchy levels (business entities, managerial entities, etc.), algorithms and special procedures have been developed.

scholarly journals A Confrontation Decision-Making Method with Deep Reinforcement Learning and Knowledge Transfer for Multi-Agent System

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 631
Author(s):  
Chunyang Hu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Knowledge Transfer ◽  
Large Scale ◽  
Effective Control ◽  
Small Scale ◽  
Learning Agent ◽  
Multi Agent ◽  
Transfer Method ◽  
Parameter Sharing

In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm with parameter sharing is proposed to achieve confrontation decision-making of multi-agent. In the process of training, the information of other agents is introduced to the critic network to improve the strategy of confrontation. The parameter sharing mechanism can reduce the loss of experience storage. In the DDPG algorithm, we use four neural networks to generate real-time action and Q-value function respectively and use a momentum mechanism to optimize the training process to accelerate the convergence rate for the neural network. Secondly, this paper introduces an auxiliary controller using a policy-based reinforcement learning (RL) method to achieve the assistant decision-making for the game agent. In addition, an effective reward function is used to help agents balance losses of enemies and our side. Furthermore, this paper also uses the knowledge transfer method to extend the learning model to more complex scenes and improve the generalization of the proposed confrontation model. Two confrontation decision-making experiments are designed to verify the effectiveness of the proposed method. In a small-scale task scenario, the trained agent can successfully learn to fight with the competitors and achieve a good winning rate. For large-scale confrontation scenarios, the knowledge transfer method can gradually improve the decision-making level of the learning agent.

Second-order consensus for a class of uncertain multi-agent systems subject to input saturation

2018 ◽  
Vol 41 (7) ◽  
pp. 1957-1964 ◽  
Author(s):  
Ming-Can Fan ◽  
Miaomiao Wang
Keyword(s):  
A Priori ◽  
Input Saturation ◽  
Second Order ◽  
Control Technique ◽  
Control Input ◽  
Multi Agent Systems ◽  
State Information ◽  
Agent Systems ◽  
Barbalat’S Lemma ◽  
Multi Agent

This paper investigates the leaderless and leader-following consensus problem for a class of second-order multi-agent systems subject to input saturation, that is, the control input is required to be a priori bounded. Moreover, the control coefficients are assumed to be unavailable, which cannot be lower or upper bounded by any known constants. Distributed consensus protocols are proposed based only on agents’ own velocity state information and relative position state information among neighbouring agents and the leader. By virtue of the adaptive control technique, algebraic graph theory and Barbalat’s lemma, it is proved that the states of the multi-agent systems can achieve consensus under the assumption that the interconnection topology is undirected and connected. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results.

scholarly journals Trust in multi-agent systems

2004 ◽  
Vol 19 (1) ◽  
pp. 1-25 ◽  
Author(s):  
SARVAPALI D. RAMCHURN ◽  
DONG HUYNH ◽  
NICHOLAS R. JENNINGS
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Comprehensive Treatment ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
The Core ◽  
Open Distributed Systems ◽  
Multi Agent

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

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