Expertness framework in multi-agent systems and its application in credit assignment problem

One of the challenging problems in artificial intelligence is credit assignment which simply means distributing the credit among a group, such as a group of agents. We made an attempt to meet this problem with the aid of the reinforcement learning paradigm. In this paper, expertness framework is defined and applied to the multi-agent credit assignment problem. In the expertness framework, the critic agent, who is responsible for distributing credit among agents, is equipped with learning capability, and the proposed credit assignment solution is based on the critic to learn to assign a proportion of the credit to each agent, and the used proportion should be learned by reinforcement learning. The paper also reports the degree of expertness framework robustness and the amount of performance decline in noisy environments. Experimental results show the superiority of the method over the common methods of credit assignment used in lots of different domains and also show that performance reduction with respect to the quantity of the noise is tolerable and the system ultimately converges to the stable and correct behavior, therefore the agents are still capable of efficiently performing in the noisy environments.