Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Market-oriented programming: some early lessons
Market-based control
Foundations of distributed artificial intelligence
Foundations of distributed artificial intelligence
Coalitions among computationally bounded agents
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Emergence: from chaos to order
Emergence: from chaos to order
Methods for task allocation via agent coalition formation
Artificial Intelligence
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Agent compatibility and coalition formation: investigating two interacting negotiation strategies
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
Simulation experiences with an ecological approach for pervasive service systems
Proceedings of the 2nd workshop on Bio-inspired algorithms for distributed systems
A self-organizing architecture for pervasive ecosystems
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
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One focus of multi-agent systems research is the notion that complex outcomes or behaviours may be arrived at through the interaction of agents. However, it is still an open question as to how agents in a complex system form coalitions or modules, and how these coalitions self-organize into hierarchies. In this paper, we begin to address this question by investigating agent collaboration in the context of a high-level pattern recognition task. We propose a novel market-based communication protocol, which governs the aggregate behaviour of individual agents and subsequent emergent properties of the system. Based on the Contract Net Protocol, individual agents bid to join coalitions (or solutions to a given problem). An important contribution of this study is the analysis of the role heterogeneous agents play in the formation of coalitions. Using a simple model, we show that by promoting diversity within the agent population it is possible to avoid deadlock or "tie" conditions, which otherwise have to be solved arbitrarily by the deadlocked agents.