SOAR: an architecture for general intelligence
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
Autonomous Agents and Multi-Agent Systems
Simulation and reinforcement learning with soccer agents
Multiagent and Grid Systems - Innovations in intelligent agent technology
Journal of Artificial Intelligence Research
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
Team formation and optimization for service provisioning
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
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Multi-agent teaming is a key research field of multi-agent systems. BDI (Belief, Desire, and Intension) architecture has been widely used to solve complex problems. The theory of joint behavior has been widely used to solve the team level optimisation problems. Due to the inherent complexity of real-time and dynamic environments, it is often extremely complex and difficult to formally specify the joint behavior of the team a priori. This paper presents a role-based BDI framework to facilitate cooperation and coordination problems. This BDI framework is extended and based on the commercial agent software development environment known as JACK Teams. A real-time 2D simulation environment known as soccerbots has been used to investigate the difficulties of multi-agent teaming. The layered architecture has been used to group the agents' competitive and cooperative behaviors, which can be learned through experience by using the reinforcement learning techniques.