Experiences Acquired in the Design of RoboCup Teams: A Comparison of Two Fielded Teams

Authors:
Stacy Marsella;Milind Tambe;Jafar Adibi;Yaser Al-Onaizan;Gal A. Kaminka;Ion Muslea
Affiliations:
Information Sciences Institute and Computer Science Department, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292 marsella@isi.edu;Information Sciences Institute and Computer Science Department, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292 tambe@isi.edu;Information Sciences Institute and Computer Science Department, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292;Information Sciences Institute and Computer Science Department, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292;Information Sciences Institute and Computer Science Department, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292;Information Sciences Institute and Computer Science Department, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292
Venue:
Autonomous Agents and Multi-Agent Systems
Year:
2001

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Abstract

Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. Effective agent interactions in such domains raise some of the most fundamental research challenges for agent-based systems, in teamwork, multi-agent learning and agent modelling. The RoboCup research initiative, particularly the simulation league, has been proposed to pursue such multi-agent research challenges, using the common testbed of simulation soccer. Despite the significant popularity of RoboCup within the research community, general lessons have not often been extracted from participation in RoboCup. This is what we attempt to do here. We have fielded two teams, ISIS97 and ISIS98, in RoboCup competitions. These teams have been in the top four teams in these competitions. We compare the teams, and attempt to analyze and generalize the lessons learned. This analysis reveals several surprises, pointing out lessons for teamwork and for multi-agent learning.