An Implementation of Large-Scale Holonic Multi-agent Society Simulator and Agent Behavior Model

2018 ◽  
pp. 1031-1043
Author(s):  
Takayuki Ito ◽  
Takanobu Otsuka ◽  
Teruyoshi Imaeda ◽  
Rafik Hadfi
Keyword(s):  
Large Scale ◽  
Behavior Model ◽  
Agent Behavior ◽  
Multi Agent

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.

Hierarchical nearly cyclic pursuit for consensus in large-scale multi-agent systems

2017 ◽  
Vol 11 (5) ◽  
pp. 740-746 ◽  
Author(s):  
Muhammad Iqbal ◽  
John Leth ◽  
Trung Dung Ngo
Keyword(s):  
Large Scale ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Cyclic Pursuit ◽  
Multi Agent

An E2GPGP-GASA-Based Multi-Agent Job Shop Scheduling System

2012 ◽  
Vol 505 ◽  
pp. 65-74
Author(s):  
Lin Lin Lu ◽  
Xin Ma ◽  
Ya Xuan Wang
Keyword(s):  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Simulation Software ◽  
Prototype System ◽  
Shop Scheduling ◽  
Model Combining ◽  
Proposed Model ◽  
Multi Agent ◽  
Global Planning

In this paper, a job shop scheduling model combining MAS (Multi-Agent System) with GASA (Simulated Annealing-Genetic Algorithm) is presented. The proposed model is based on the E2GPGP (extended extended generalized partial global planning) mechanism and utilizes the advantages of static intelligence algorithms with dynamic MAS. A scheduling process from ‘initialized macro-scheduling’ to ‘repeated micro-scheduling’ is designed for large-scale complex problems to enable to implement an effective and widely applicable prototype system for the job shop scheduling problem (JSSP). Under a set of theoretic strategies in the GPGP which is summarized in detail, E2GPGP is also proposed further. The GPGP-cooperation-mechanism is simulated by using simulation software DECAF for the JSSP. The results show that the proposed model based on the E2GPGP-GASA not only improves the effectiveness, but also reduces the resource cost.

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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