Massively Multi-Agent Technology: AAMAS Workshops, MMAS 2006, LSMAS 2006, and CCMMS 2007 Hakodate, Japan, May 9, 2006 Honolulu, HI, USA, May 15, 2007, Selected and Revised Papers
With the proliferation of consumer devices, computation is becoming truly ubiq- tous. Multi-agent systems hold the promise to enable utilization of these compu- tional resources for ground-breaking new applications. However, as the number of agents involved in multi-agent computations rises, traditional approaches of bui- ing multi-agent systems fail to scale. Massively multi-agent systems address this challenge of scale. Massive, in this context, is meant to capture the complexity of such systems, which precludes decision making to focus on individual agents. Agents making decisions have to reduce the complexity of the space in which they must decide. The focus, therefore, is on the approaches to manage the compl- ity, implications of this abstraction, as well as on identifying ways of applying the paradigm to problems. Papers appearing in this volume have been selected from three inter- tional workshops held in conjunction with the International Conferences on - tonomous Agents and Multiagent Systems (AAMAS) held in 2006 and 2007: First International Workshop on Coordination and Control in Massively Mul- agent Systems (CCMMS 2007); and the jointly held Second International Workshop on Massively Multi-agent Systems (MMAS 2006) and the Third Int- national Workshop on Challenges in the Coordination of Large-Scale Multi-agent Systems (LSMAS 2006).
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A Platform for Massive AgentBased Simulation and Its Evaluation
Distributed Coordination of Massively MultiAgent Systems
CommunityBased Load Balancing for Massively MultiAgent Systems
A Study of Coordinated Dynamic MarketBased Task Assignment in Massively MultiAgent Systems
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Evolve Individual Agent Strategies to Global Social Law by Hierarchical Immediate Diffusion
Team Formation Strategies in a Dynamic LargeScale Environment
Using SwarmGAP for Distributed Task Allocation in Complex Scenarios
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adaptive agent system agent-based algorithm approach Artiﬁcial Autonomous Agents blades complete computation conﬁguration cooperative coordinated action cyberorg DCOP deﬁned detection diﬀerent diffusion interaction distributed distributed constraint optimization dynamic utility functions dynamicism eCash edge detection evaluation ﬁeld ﬁnd ﬁrst forward auction global social law IEEE image segmentation inter-server interaction cost International job selection heuristic junior agent LA-DCOP large number load balancing Massively Multi-Agent Systems master facilitator mechanism methods MMAS moving multi-agent systems multiple NetManager-M network agent nodes noise region number of agents number of messages number of tasks parameters performance pheromone pixels planar region potential energy problem processor range image request reverse auction RoboCup robot agents Runtime Agent Runtime scalability scenario scheduling seller sensors set of agents simulation environment slaves social strategies speciﬁc swapping swarm Swarm Intelligence Swarm-GAP target task allocation task instances task selection team formation threshold update ZASE