SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Modeling Organizational Rules in the Multi-agent Systems Engineering Methodology
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Self-organization through bottom-up coalition formation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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 Case Study of Organizational Effects in a Distributed Sensor Network
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A Dynamically Formed Hierarchical Agent Organization for a Distributed Content Sharing System
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
The impact of vertical specialization on hierarchical multi-agent systems
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Using IDEF0 to enhance functional analysis in MOISE+ organizational modeling
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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As the scale and scope of multi-agent systems grow, it becomes increasingly important to manage the manner in which the participants interact. The potential for bottlenecks, intractably large sets of coordination partners, and shared bounded resources can make individual and high-level goals difficult to achieve. To address these problems, many large systems employ an additional layer of structuring, known as an organizational design, that assigns agents particular and different roles, responsibilities and peers. These additional constraints can allow agents to operate effectively within a large-scale system. In this paper, we will introduce a domain-independent organizational design representation capable of modeling and predicting the quantitative performance characteristics of agent organizations. This representation supports the selection of an appropriate design given a particular operational context. We will demonstrate how the language can be used to represent complex interactions, and show modeling techniques that can address the combinatorics of large-scale agent systems.