SOAR: an architecture for general intelligence
Artificial Intelligence
Collaborative plans for complex group action
Artificial Intelligence
Artificial Intelligence - Special issue on Robocop: the first step
On agent-based software engineering
Artificial Intelligence
Integrating tools and infrastructures for generic multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Dynamic Programming
Simulation and reinforcement learning with soccer agents
Multiagent and Grid Systems - Innovations in intelligent agent technology
Journal of Artificial Intelligence Research
CAST: collaborative agents for simulating teamwork
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Integrating agents in software applications
NODe'02 Proceedings of the NODe 2002 agent-related conference on Agent technologies, infrastructures, tools, and applications for E-services
Reinforcement learning of competitive skills with soccer agents
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
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Multi-agent teaming is an active research field of multi-agent systems. Flexible multi-agent decision making requires effective reaction and adaption to dynamic changes under time pressure, especially in real-time and dynamic systems. The joint intension and sharedplan are two most popular theories for the teamwork of multi-agent systems. However, there is no clear guidance for designing and implementing agents' teaming. BDI (Belief, Desire, and Intension) architecture has been widely used to design multi-agent systems. In this paper, a role-based BDI framework is presented to facilitate the team level optimization problems such as competitive, cooperation and coordination problems. This BDI framework is extended on the commercial agent software development environment known as JACK Teams. The layered architecture has been used to group the agents' competitive and cooperative behaviors. In addition, we present the reinforcement learning techniques to learn different behaviors through experience. These issues have been specified and investigated within a real-time 2D simulation environment known as soccerBots.