Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
From Local Behaviors to Global Performance in a Multi-Agent System
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Journal of Artificial Intelligence Research
Knowledge discovery for training intelligent agents: methodology, tools and applications
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Data mining techniques for robocup soccer agents
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
A formal immune network and its implementation for on-line intrusion detection
MMM-ACNS'05 Proceedings of the Third international conference on Mathematical Methods, Models, and Architectures for Computer Network Security
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This paper describes a possibility of applying on-line learning techniques to train agents for teamwork. Special modules proposed based on immunological networks capable of on-line learning in dynamically changing environments. The above modules provide adaptive agents' behavior for teamwork after they are trained to select of primitive behaviors under variable environmental conditions. Reinforcement learning is considered to be the main method for training agents during a game. The special agent capable of on-line training for basketball competitions in RoboFIBA virtual system was developed and investigated. Examples of immunological networks for agent teamwork implementation are considered, and results of experiment with hard and training agents are described.