C4.5: programs for machine learning
C4.5: programs for machine learning
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Team-partitioned, opaque-transition reinforcement learning
Proceedings of the third annual conference on Autonomous Agents
Artificial Intelligence - Special issue on Robocop: the first step
RoboCup-97: Robot Soccer World Cup I
RoboCup-97: Robot Soccer World Cup I
RoboCup-98: Robot Soccer World Cup II
RoboCup-98: Robot Soccer World Cup II
Using an Explicit Model of Teamwork in RoboCup-97
RoboCup-97: Robot Soccer World Cup I
Layered learning in multiagent systems
Layered learning in multiagent systems
The RoboCup synthetic agent challenge 97
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
RoboCup 2001: Robot Soccer World Cup V
Strategy Learning for a Team in Adversary Environments
RoboCup 2001: Robot Soccer World Cup V
The CMUnited-99 Champion Simulator Team
RoboCup-99: Robot Soccer World Cup III
CG '00 Revised Papers from the Second International Conference on Computers and Games
Understanding decentralised control of resource allocation in a minimal multi-agent system
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Emergent service provisioning and demand estimation through self-organizing agent communities
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Coordination by design and the price of autonomy
Autonomous Agents and Multi-Agent Systems
Learning from demonstration with swarm hierarchies
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Self-organizing agent communities for autonomic resource management
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
| Hi-index | 0.00 |
RoboCup was introduced as a challenge area at IJCAI-97. We have been actively pursuing research in this area and have participated in the RoboCup competitions, winning the RoboCup-98 and RoboCup-99 simulator competitions. In this paper, we report on the main technical issues that we encountered and addressed in direct response to the learning and teamwork challenges stated in the IJCAI-97 challenge paper. We describe "layered learning" in which off-line and online, individual and collaborative, learned robotic soccer behaviors are combined hierarchically. We achieve effective teamwork through a team member agent architecture that encompasses a "flexible teamwork structure." Agents are capable of decomposing the task space into flexible roles and can switch roles while acting. We report detailed empirical results verifying the effectiveness of the learned behaviors and the components of the team member agent architecture.