Multi-Agent systems has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. One of the most important aspects of agent design in AI is the way agent acts or responds to the environment that the agent is acting upon. An effective action selection and behavioral method gives a powerful advantage in overall agent performance. We define a new method of action selection based on probability/priority models in RoboCup Soccer Simulation League[6] and for simulated soccer agents, we thereby introduce an efficient way to determine probabilities and a new priority based system which maps the human knowledge to action selection method. Furthermore, a behavior model is introduced to make the model more flexible.