@magazinearticle {1510810,
	title = {Building agent teams using an explicit teamwork model and learning},
	journal = {Artificial Intelligence},
	volume = {110},
	year = {1999},
	pages = {215-240},
	abstract = {
	Multi-agent collaboration or teamwork and learning are two critical research challenges in a large number of multi-agent applications. These research challenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research in multi-agent systems. This article describes our approach to address these challenges, based on a team of soccer-playing agents built for the simulation league of RoboCup{\textemdash}the most popular of the RoboCup leagues so far.



	To address the challenge of teamwork, we investigate a novel approach based on the (re)use of a domain-independent, explicit model of teamwork, an explicitly represented hierarchy of team plans and goals, and a team organization hierarchy based on roles and role-relationships. This general approach to teamwork, shown to be applicable in other domains beyond RoboCup, both reduces development time and improves teamwork flexibility. We also demonstrate the application of\&nbsp;off-line\&nbsp;and\&nbsp;on-line\&nbsp;learning to improve and specialize agents{\textquoteright} individual skills in RoboCup. These capabilities enabled our soccer-playing team, ISIS, to successfully participate in the first international RoboCup soccer tournament (RoboCup{\textquoteright}97) held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league.

},
	author = {Tambe, Milind and Jafar Adibi and Yasar Alonaizan and Ali Erdem and Gal Kaminka and Ion Muslea and Marsella, Stacy}
}
