Exploiting Opportunities through Dynamic Coalitions in Robotic Soccer

While the coordination of multiple agents in static networks or hierarchical relationships has been the subject of much research over a long period of time (e.g. see [1]), the relationships between agents in dynamic multi-agent systems has only comparatively recently been the subject of significant investigation. In dynamic multi-agent systems, the nature of relationships between agents can change, possibly along with the goals of a group. In such systems, the environment surrounding the agents may change, altering the optimality of interactions (e.g. a change in opponent agents; a change in optimal strategy leading to different relationships between agents). While we may have preferences for the way in which agents interact (or even some agents in authority to rely upon), there is no hard-and-fast answer to who should optimally interact with whom and under what conditions. Agents may seek out momentary coalitions with others, or form longer-term bonds that are flexible enough to support some degree of interaction under a wide range of circumstances.

[1]  Alan H. Bond,et al.  Readings in Distributed Artificial Intelligence , 1988 .

[2]  Hiroaki Kitano,et al.  The RoboCup Synthetic Agent Challenge 97 , 1997, IJCAI.

[3]  Ian Frank,et al.  Soccer Server: A Tool for Research on Multiagent Systems , 1998, Appl. Artif. Intell..

[4]  Manuela M. Veloso,et al.  Task Decomposition and Dynamic Role Assignment for Real-Time Strategic Teamwork , 1998, ATAL.

[5]  R. Arkin,et al.  Behavioral diversity in learning robot teams , 1998 .

[6]  Peter Stone,et al.  RoboCup 2000: Robot Soccer World Cup IV , 2001, RoboCup.

[7]  Gaurav S. Sukhatme,et al.  Most valuable player: a robot device server for distributed control , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[8]  B. Tanner,et al.  Peer Reinforcement in Homogeneous and Heterogeneous Multi-agent Learning , 2002 .