Application of Multi-agent Technology in Decision-making System for Vessel Automatic Anti-collision

2007 ◽  
Author(s):  
Shenhua Yang ◽  
Chaojian Shi ◽  
Yuhong Liu ◽  
Qinyou Hu
Keyword(s):  
Decision Making ◽  
Agent Technology ◽  
Decision Making System ◽  
Multi Agent

Multi-Agent Decision Making System based on Membrane Computing

2021 ◽  
Author(s):  
Silvia Munteanu ◽  
Viorica Sudacevschi ◽  
Victor Ababii ◽  
Olesea Borozan ◽  
Constantin Ababii ◽  
...  
Keyword(s):  
Decision Making ◽  
Membrane Computing ◽  
Decision Making System ◽  
Multi Agent

ProPlanT as a Multi-Agent Technology for Decision-Making Support

2010 ◽  
pp. 1287-1294
Author(s):  
Vladimir Marík ◽  
Michal Pechoucek ◽  
Jiri Vokrínek
Keyword(s):  
Decision Making ◽  
Computational Model ◽  
Production Planning ◽  
Agent Technology ◽  
Planning And Scheduling ◽  
Distributed Structure ◽  
Production Planning And Scheduling ◽  
Decision Making Problem ◽  
Multi Agent ◽  
Decision Making Support

Production planning and resource allocation is a complex industrial decision-making problem. Sophisticated computational model of a manufacturing domain may support this decision making by simulation of multiple variants of alternative plans and thus help identifying the most suitable one (according to defined conditions). Multi-agent system is an example of such a computational model as it can naturally represent the hierarchical and distributed structure of the manufacturing enterprise that is modelled. This item presents and discusses ProPlanT (Marík, Pechoucek, Štepánková, & Lažanský, 2000), a specific multi-agent technology/methodology for production planning and scheduling in the manufacturing domain. This methodology resulted in a framework for decision making support which was successfully applied in several pioneer applications.

An Agent-Based Knowledge Management Framework for Marketing-Mix Decision Making

2015 ◽  
pp. 482-502
Author(s):  
Masoomeh Moradi ◽  
Abdollah Aghaie ◽  
Monireh Hosseini
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Business Environment ◽  
Marketing Mix ◽  
Agent Technology ◽  
Multi Agent System ◽  
Agent System ◽  
Management Framework ◽  
Agent Based ◽  
Multi Agent

Marketing-mix plays an essential role in the competitive business environment. Marketing decision makers constantly need to monitor changes in the environment and organization to make necessary changes. Therefore, a knowledge management system is required to acquire, store, retrieve and use up-to-dated knowledge. Corporations also tend to look for systems assisting them in knowledge management. Agent technology looks set for assisting organizations in collecting, processing and using knowledge with high accuracy, speed and efficiency. This paper proposes a knowledge management framework for marketing-mix decision making through using agent technology. A multi-agent system is deployed to acquire, refine, store, retrieve, present, show and update the related knowledge of marketing-mix decision making. The fuzzy logic is applied by multi-agent system to make decision. Implementation of the proposed system in a car factory indicates that it is efficient and effective in supporting and improving marketing-mix decision making.

scholarly journals Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 341 ◽  
Author(s):  
Hu ◽  
Xu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Action Function ◽  
State Action ◽  
Markov Decision ◽  
Decision Making System ◽  
Multi Agent ◽  
Function Approximator ◽  
Multi Robot

Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.

An Agent-Based Knowledge Management Framework for Marketing-Mix Decision Making

2013 ◽  
Vol 4 (3) ◽  
pp. 109-128 ◽  
Author(s):  
Masoomeh Moradi ◽  
Abdollah Aghaie ◽  
Monireh Hosseini
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Business Environment ◽  
Marketing Mix ◽  
Agent Technology ◽  
Multi Agent System ◽  
Agent System ◽  
Management Framework ◽  
Agent Based ◽  
Multi Agent

Marketing-mix plays an essential role in the competitive business environment. Marketing decision makers constantly need to monitor changes in the environment and organization to make necessary changes. Therefore, a knowledge management system is required to acquire, store, retrieve and use up-to-dated knowledge. Corporations also tend to look for systems assisting them in knowledge management. Agent technology looks set for assisting organizations in collecting, processing and using knowledge with high accuracy, speed and efficiency. This paper proposes a knowledge management framework for marketing-mix decision making through using agent technology. A multi-agent system is deployed to acquire, refine, store, retrieve, present, show and update the related knowledge of marketing-mix decision making. The fuzzy logic is applied by multi-agent system to make decision. Implementation of the proposed system in a car factory indicates that it is efficient and effective in supporting and improving marketing-mix decision making.

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