Priority Extraction Using Delayed Rewards in Multi Agents Systems : A Case Study in RoboCup

multi - agent systems has generated lots of excitement in re-cent 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 behavioural method requires a good priority extraction method for finding the best actions . we have introduced an easy way for doing such a thing using delayed rewards. we have also applied the method to the rbocup rescue simulation agents.