Designing organizations for computational agents
Simulating organizations
A Model of Almost Everything: Norms, Structure and Ontologies in Agent Organizations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
A survey of multi-agent organizational paradigms
The Knowledge Engineering Review
Quantitative organizational modeling and design for multi-agent systems
Quantitative organizational modeling and design for multi-agent systems
Strongly typed genetic programming
Evolutionary Computation
Genetic algorithm aided optimization of hierarchical multiagent system organization
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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In this paper, we proposed Evolutionary Organizational Search (EOS), an optimization method for the organizational control of multi-agent systems (MASs) based on genetic programming (GP). EOS adds to the existing armory a metaheuristic extension, which is capable of efficient search and less vulnerable to stalling at local optima than greedy methods due to its stochastic nature. EOS employs a flexible genotype which can be applied to a wide range of tree-shaped organizational forms. EOS also considers special constraints of MASs. A novel mutation operator, the redistribution operator, was proposed. Experiments optimizing an information retrieval system illustrated the adaptation of solutions generated by EOS to environmental changes.