Training of Agent Positioning Using Human’s Instruction
Hidehisa Akiyama*, Daisuke Katagami**, and Katsumi Nitta**
*National Institute of Advanced Industrial Science and Technology
**Tokyo Institute of Technology
In the real-world multiagent/multirobot problems, a position of each agent is an important factor to affect agents’ performance. In the real-world problem such as soccer, the agent(player)’s position should be changed based on the current environmental state. Because the real-world problem is generally dynamic and continuous, assigning the most desirable position for any state is not possible. We formalize this issue as a map from a focal point like a ball position in a soccer field to a desirable position of each agent. We conducted experiments showing that agent positioning are acquired efficiently through intuitive human operation and function approximation models using supervised learning, and conducted performance evaluation experiments to determine the most suitable model. In performance evaluation experiments, we evaluated the generalization capability for each model with datasets which several data are randomly removed from original dataset and dataset which several specific data are intentionally removed from original dataset. Experiment results showed that our proposed function approximation model combining Delaunay triangulation and linear interpolation produced the highest performance.
-  “The RoboCup Soccer Simulator,”
-  H. Akiyama and I. Noda, “Agent Positioning Mechanism in the RoboCup Soccer Simulation,” In IPSJ SIG-GI-17, pp. 9-16, 2007.
-  H. Gouraud, “Continuous shading of curved surfaces,” In R. Wolfe (Edit), Seminal Graphics: Pioneering efforts that shaped the field, ACM Press, 1998.
-  M. v. K. M. d. Berg, O. Schwarzkopf and M. Overmars, “Computational Geometry: Algorithms and Applications,” Springer Verlag, second edition, 2000.
-  J. Moody and C. Darken, “Fast learning in networks of locallytuned processing units,” No.1, pp. 289-303, 1989.
-  I. Noda and H. Matsubara, “Soccer Server and Researches on Multi-Agent Systems,” In H. Kitano (Edit), Proc. of IROS-96 Workshop on RoboCup, pp. 1-7, Nov. 1996.
-  T. Poggio and F. Girosi, “Networks for approximation and learning,” No.78, pp. 1481-1497, 1990.
-  L. P. Reis, N. Lau, and E. Olivéira, “Situation Based Strategic Positioning for Coordinating a Simulated RoboSoccer Team,” Balancing Reactivity and Social Deliberation in MAS, pp. 175-197, 2001.