Global Planning from Local Eyeshot: An Implementation of Observation-Based Plan Coordination in RoboCup Simulation Games

This paper presents a method of implementing distributed planning in partial-observable, low communication bandwidth environments such as RoboCup simulation games, namely, the method of global planning from local perspective. By concentrating planning on comprehending and maximizing global utility, the method solved the problem that individual effort might diverge against team performance even in cooperative agent groups. The homogeny of the agent decision architecture and the internal goal helps to create privities among agents, thus make the method applicable. This paper also introduces the application of this method in the defense system of Tsinghuaolus, the champion of RoboCup 2001 Robot Soccer World Cup Simulation League.

[1]  Luís Paulo Reis,et al.  FC Portugal Team Description: RoboCup 2000 Simulation League Champion , 2000, RoboCup.

[2]  Peter Stone,et al.  Layered Learning in Multiagent Systems , 1997, AAAI/IAAI.

[3]  S. A. Long,et al.  Fuzzy BDI architecture for social agents , 2000, Proceedings of the IEEE SoutheastCon 2000. 'Preparing for The New Millennium' (Cat. No.00CH37105).

[4]  Gerhard Weiss,et al.  Multiagent Systems , 1999 .

[5]  Frank Dignum,et al.  Towards socially sophisticated BDI agents , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[6]  Victor R. Lesser,et al.  Cooperative Multiagent Systems: A Personal View of the State of the Art , 1999, IEEE Trans. Knowl. Data Eng..

[7]  Masayuki Ohta,et al.  Learning Cooperative Behaviors in RoboCup Agents , 1997, RoboCup.

[8]  Gerhard Weiss,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 1999 .

[9]  Manuela M. Veloso,et al.  Layered Learning and Flexible Teamwork in RoboCup Simulation Agents , 1999, RoboCup.