Coaching a simulated soccer team by opponent model recognition
Proceedings of the fifth international conference on Autonomous agents
Recognizing Probabilistic Opponent Movement Models
RoboCup 2001: Robot Soccer World Cup V
Recognizing Formations in Opponent Teams
RoboCup 2000: Robot Soccer World Cup IV
Defining and Using Ideal Teammate and Opponent Agent Models
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Discovering tactical behavior patterns supported by topological structures in soccer agent domains
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Opponent Provocation and Behavior Classification: A Machine Learning Approach
RoboCup 2007: Robot Soccer World Cup XI
OMBO: An opponent modeling approach
AI Communications
The RoboCup synthetic agent challenge 97
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Recognizing Team Formations in Multiagent Systems: Applications in Robotic Soccer
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Analysis and Forecast of Team Formation in the Simulated Robotic Soccer Domain
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Classifying agent behaviour through relational sequential patterns
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Learning action descriptions of opponent behaviour in the robocup 2D simulation environment
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Predicting opponent actions by observation
RoboCup 2004
A comparing method of two team behaviours in the simulation coach competition
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
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This paper reviews the proposed opponent modeling algorithms within the soccer simulation domain. RoboCup soccer simulation 2D is a rich multi agent environment where opponent modeling plays a crucial role. In multi agent systems with adversarial and cooperative agents, team agents should be adapted to the current environment and opponent in order to propose appropriate and effective counteractions. Predicting the opponent's future behaviors during competition allows for more informed decisions. We divide opponent modeling into two categories of individual agent behaviors and team behaviors. Individual behaviors concern modeling the low-level behaviors of individual opponent agents, however in team behaviors, the high-level strategy of the entire team like formation, offensive and defensive system, is recognized. Several methods have been proposed to create different models of opponents to improve the performance of teams in an essential aspect. In this paper, we review the approaches to the problem of opponent modeling published from 2000 to 2010.