Ranjit Nair, Milind Tambe, and Stacy Marsella
Our work in team formation focuses on dynamic, realtime environments, such as sensor networks (Modi et al. 2001) and RoboCup Rescue Simulation Domain (Kitano et al. 1999; Tadokoro et al. 2000). In such domains teams must be formed rapidly so tasks are performed within given deadlines, and teams must be reformed in response to the dynamic appearance or disappearance of tasks. The problems with the current team formation work for such dynamic real-time domains are two-fold: i) most team formation algorithms (Tidhar, Rao, and Sonenberg 1996; Hunsberger and Grosz 2000; Fatima and Wooldridge 2001; Horling, Benyo, and Lesser 2001, Modi et al. 2001) are static. In order to adapt to the changing environment the static algorithm would have to be run repeatedly, ii) Team formation has largely relied on experimental work, without any theoretical analysis of key properties of team formation algorithms, such as their worst-case complexity. This is especially important because of the real-time nature of the domains.