A Cooperation Model Using Reinforcement Learning for Multi-agent.
ABSTRACT In multi-agent systems, the common goals of each agent are established and the problems are solved through cooperation and
control among agents. Because each agent performs parallel processes in a multi-agent system, this approach can be easily
applied to problems requiring parallel processing. The parallel processing prevents system performance degradation due to
local error operation in the system. It also can reduce the solution time when the problem is divided into several sub-problems.
In this case, each agent is designed independently providing a relatively simple programming model for solution of the problem.
Further, the system can be easily expanded by adding new function agents. In the study of multi-agent systems, the main research
topic is the coordination and cooperation among agents.
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ABSTRACT: Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goal; Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the tec...Autonomous Robots 05/2000; · 1.75 Impact Factor
Article: Software Agents: A review05/1999;
Conference Paper: Co-ordination in Multi-Agent Systems.Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications; 01/1997