In a collaborative environment, knowledge about collaborators' skills is an important factor when determining which team members should perform a task. However, this knowledge may be incomplete or uncertain. In this paper, we extend our ETAPP (Environment-Task-Agents-Policy-Protocol) collaboration framework by modeling team members that exhibit non-deterministic performance, and comparing two alternative ways of using these models to assign agents to tasks. Our simulation-based evaluation shows that performance variability has a large impact on task performance, and that task performance is improved by consulting agent models built from a small number of observations of agents' recent performance. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Zukerman, I., & Guttmann, C. (2005). Modeling agents that exhibit variable performance in a collaborative setting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3538 LNAI, pp. 210–219). Springer Verlag. https://doi.org/10.1007/11527886_27
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