Multiagent Systems: A Survey from a Machine Learning Perspective Export

@article{Stone2000, 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 techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.}, author = {Stone, Peter and Veloso, Manuela}, citeulike-article-id = {3149636}, comment = {not reviewed}, journal = {Autonomous Robots}, keywords = {communication, intelligent-agents, machine-learning, multiagent-systems, robotic-soccer, robotics}, month = {June}, number = {3}, pages = {345--383}, posted-at = {2008-08-22 17:09:57}, priority = {2}, title = {Multiagent Systems: A Survey from a Machine Learning Perspective}, volume = {8}, year = {2000} }

TY - JOUR ID - Stone2000 L3 - citeulike-article-id:3149636 N2 - 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 techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue. IS - 3 JF - Autonomous Robots EP - 383 TI - Multiagent Systems: A Survey from a Machine Learning Perspective VL - 8 SP - 345 KW - communication KW - intelligent-agents KW - machine-learning KW - multiagent-systems KW - robotic-soccer KW - robotics N1 - not reviewed AU - Stone, Peter AU - Veloso, Manuela PY - 2000/June// ER -