RoboCup Opponent Modeling Challenge

Agent modeling - modeling and reasoning about other agent's goals, plans, knowledge, capabilities, or emotions -- is a key issue in multi-agent interaction. The RoboCup opponent modeling challenge calls for research on modeling a team of opponents in a dynamic, multi-agent domain. The modeling issues in RoboCup can be broken down into three parts:

On-line tracking:

Involves individual players' real-time, dynamic tracking of opponents' goals and intentions based on observations of actions. A player may use such tracking to predict the opponents' play and react appropriately. Thus if a player predicts that player-5 is going to pass a ball to player-4, then it may try to cover player-4. Such on-line tracking may also be used in service of deception. The challenges here are (i) real-time tracking despite the presence of ambiguity; (ii) addressing the dynamism in the world; (iii) tracking teams rather than only individuals - this requires an understanding of concepts involved in teamwork.

On-line tracking may feed input to the on-line planner or the on-line learning alogrithm.

On-line strategy recognition:

"Coach" agents for teams may observe a game from the sidelines, and understand the high-level strategies employed by the opposing team. This contrasts with on-line tracking because the coach can perform a much higher-level, abstract analysis, and in the absence of real-time pressures, its analysis can be more detailed.

The coach agents may then provide input to its players to change the team formations, or play strategy.

Off-line review:

"Expert" agents may observe the teams playing in an after-action review, to recognize the strenghts and weaknesses of the teams, and provide an expert commentary. These experts may be trained on databases of human soccer play.

These issues pose some fundamental challenges that will significantly advance the state of the art in agent modeling. In particular, previous work has mostly focused on plan recognition in static, single-agent domains, without real-time constraints. Only recently has attention shifted to dynamic, real-time environments, and modeling of multi-agent teamwork [].

A realistic challenge for IJCAI-99 will be to aim for on-line tracking. Optimistically, we expect some progress towards on-line strategy recognition; off-line review will likely require further research beyond IJCAI-99.

For evaluation, we propose, at least, following evaluation to be carried out to measure the progress:

Game Playing:

A team of agents plays against two types of teams:

• One or two unseen RoboCup team from IJCAI-97, shielded from public view.
• The same unseen RoboCup teams from IJCAI-97 as above, but modified with some new behaviors. These teams will now deliberately try out new adventurous strategies, or new defensive strategies.

Disabled Tracking:

Tracking functionality of the agents will be turned off, and compared with normal performance.

Deceptive Sequences:

Fake teams will be created which generates deceptive moves. The challenger's agent must be able to recognize the opponent's deceptive moves to beat this team.

For each type of team, we will study the performance of the agent-modelers. Of particular interest is variations seen in agent-modelers behaviors given the modification in the opponents' behaviors. For each type of team, we will also study the advise offered by the coach agent, and the reviews offered by the expert agents, and the changes in them given the changes in the opponents' behaviors.