Abstract
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.
This material is based upon work supported by the National Science Foundation under Grant No. IIS-9988784. This material is also based upon work supported by the National Science Foundation Engineering Research Centers Program under NSF Award No. EEC-0313747. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Horling, B., Lesser, V. (2005). Quantitative Organizational Models for Large-Scale Agent Systems. In: Ishida, T., Gasser, L., Nakashima, H. (eds) Massively Multi-Agent Systems I. MMAS 2004. Lecture Notes in Computer Science(), vol 3446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11512073_9
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DOI: https://doi.org/10.1007/11512073_9
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