Modeling and exploiting behavior patterns in dynamic environments

Ball, D. M. and Wyeth, G. F. (2004). Modeling and exploiting behavior patterns in dynamic environments. In: H. Asama, Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004. The 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), Sendai, Japan, (1371-1376). 28 September-2 October 2004. doi:10.1109/IROS.2004.1389587


Author Ball, D. M.
Wyeth, G. F.
Title of paper Modeling and exploiting behavior patterns in dynamic environments
Conference name The 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004)
Conference location Sendai, Japan
Conference dates 28 September-2 October 2004
Proceedings title Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004
Journal name 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Place of Publication Piscataway, NJ, U.S.A.
Publisher The Institute of Electrical and Electronics Engineers
Publication Year 2004
Sub-type Fully published paper
DOI 10.1109/IROS.2004.1389587
ISBN 0-7803-8463-6
Editor H. Asama
Volume 2
Start page 1371
End page 1376
Total pages 6
Collection year 2004
Language eng
Abstract/Summary This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)
Subjects E1
380305 Knowledge Representation and Machine Learning
780199 Other
Q-Index Code E1

 
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Created: Thu, 23 Aug 2007, 19:44:01 EST