next up previous
Next: Technical Issues Up: The RoboCup Learning Challenge Previous: The RoboCup Learning Challenge


The objectives of the RoboCup Learning Challenge is to solicit comprehensive learning schemes applicable to the learning of multiagent systems which need to adapt to the situation, and to evaluate the merits and demerits of proposed approaches using standard tasks.

Learning is an essential aspect of intelligent systems. In the RoboCup learning challenge, the task is to create a learning and training method for a group of agents. The learning opportunities in this domain can be broken down into several types:

  1. Off-line skill learning by individual agents;
  2. Off-line collaborative learning by teams of agents;
  3. On-line skill and collaborative learning;
  4. On-line adversarial learning.

The distinction between off-line and on-line learning is particularly important in this domain since games last for only 20 minutes. Thus on-line techniques, particularly if they are to learn concepts that are specific to an individual game, must generalize very quickly. For example, if a team is to learn to alter its behavior against an individual opponent, the team had better be able to improve its performance before the game is over and a new opponent appears. Such distinctions in learning can be applied to a broad range of multi-agent systems which involve learning capabilities.

Peter Stone
Tue Sep 23 10:34:44 EDT 1997