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Multi-agent systems (MAS) try to formulate dynamic world which surround human being in every aspect of his life. One of the important challenges encountered in multiagent systems is the credit assignment problem, simply means distributing the result of the work of a group of agents, such that every agent will have the capability of individual learning. This paper presents the result of a solution suggested for multi-agent credit assignment problem. With the help of observing history of credit assignment in the environment, we will understand what actions are reward-deserving. Results are reported on a multi agent domain, addition agents. © 2009 IEEE  

Multi-agent systems (MAS) is a solution to the nowadays encountered problems, which have the characteristics such as distributiveness, dynamism and the need to adaptation, robustness, efficiency, and reusability. This paper proposed a solution to multi-agent credit assignment problem. The contribution is to equip the critic agent (who is responsible for distributing reinforcements among agents) with learning capability. Some criteria are used to propose an inner feedback to the critic. Results of simulation show the applicability of the method to a task, which has the characteristic that the agent has to decide from a large set of actions. The research is a preliminary step to more in-depth... 

Multi-agent systems can be seen as an apparatus for testing the performance of real distributed systems. One problem encountered in multi-agent systems with the learning capability is credit assignment. This paper presents two methods for solving this problem. The first method assigns credit to the agents according to the history of the interaction while the second method assigns credit to the agents according to the knowledge of agents, and thus the shares of the agents are extracted from the feedback of the environment. The computer experiments show that critic learning has a positive impact in credit assignment problem