On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Distributed Algorithms
The Origins of Ontologies and Communication Conventions in Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems
Foundations of Computational Mathematics
The complexity of finding an optimal policy for language convergence
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Force Versus Majority: A Comparison in Convention Emergence Efficiency
Coordination, Organizations, Institutions and Norms in Agent Systems IV
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Multi-Agent Agreement Problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from distributed sensor networks, to meeting scheduling, to development of norms, conventions, and language. While much work has been done on particular agreement problems no unifying framework exists for comparing MAPs that vary in, e.g., strategy space complexity, inter-agent accessibility, and solution type, and understanding their relative complexities. We present such a unification, the Distributed Optimal Agreement (DOA) framework, and show how it captures a wide variety of agreement problems. To demonstrate DOA and its power we apply it to convention evolution. 1