CBRRoboSoc: An Efficient Planning Strategy for Robotic Soccer Using Case Based Reasoning

Authors:
T. Srinivasan;K. Aarthi;S. Aishwarya Meenakshi;M. Kausalya
Affiliations:
Sri Venkateswara College of Engineering, India;Sri Venkateswara College of Engineering, India;Sri Venkateswara College of Engineering, India;Sri Venkateswara College of Engineering, India
Venue:
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Year:
2006

Citing 0
Cited 4

Cases in Robotic Soccer

ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions

Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Agent cooperation within adversarial teams in dynamic environment --- key issues and development trends

Transactions on Compuational Collective Intelligence VI
Applying hybrid learning approach to RoboCup's strategy

Journal of Systems and Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

Over the past decade the field of Robotic Soccer has been the focus of intensive research. This paper proposes 'An Efficient Planning Strategy for Robotic Soccer using Case based Reasoning (CBRRoboSoc)' to build prototypes for a team of two soccer playing robots in the RoboCup small size league. The proposed CBRRoboSoc system develops an efficient strategy to plan both offense and defense team behavior. The system along with its field simulation, prediction and command generation phases, employing Bayesian Classifier, help the robots to plan individual moves, recognize game states, as well as to model the world of the playing field. Our model supports game play selection in key game situations, which should in turn significantly advantage the team. Further more by incorporating case base for offense team, simulation of four keep away trials illustrate optimized offense behavior.