Since cooperation is the key to success in most biological and artificial communities, the capability of cooperation in multi-agent systems is critical in achieving better solutions. Multi-agent cooperative learning results in higher efficiency and faster learning compared to individual learning, due to more resources of knowledge and information. Better cooperative strategies may speed up and improve learning. Cooperative learning is a group learning activity organized so that learning is dependent on the socially structured exchange of information between learners in groups while each learner is held accountable for his/her own learning. The aim of this thesis is to contribute to an answer to the question: "How can agents enjoy of exchanging information during the cooperative learning process, in order to achieve better individual and overall system performances?"Researches in cooperative learning showed that the question is not only: "what type of information to exchange?", but also "how to use shared information?" Nowadays, the majority of researches in multi-agent cooperative learning field focus on Reinforcement Learning (RL) as their basic learning method. RL is one of the most prominent machine learning methods due to its unsupervised learning structure and continuous learning ability, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from other agents in the system so that the speed of learning can be accelerated. During the life cycle, human learns through different experiences over different time periods of his life. Sometimes the experience is quite successful and sometimes it completely fails. Individual’s character is formed based on all of the gained experiences whether they are good or bad. Everyone will make his decisions based on his formed character. This is what we have attempted to translate into the realm of multi-agent systems learning. For this purpose, in our study a novel concept named Multi-Criteria Expertness is introduced that takes advantages of worthy information about different experiences of agents in a cooperative multi-agent system. In addition, in this thesis a new cooperative learning algorithm is proposed which enjoys of multi-criteria expertness concept and attempts to cooperate more efficiently. The proposed method has high ability to use more knowledge and information compared to existing methods which leads to high performance. the agents. Keywords: Multi-Agent Systems, Cooperative Learning, Multi-Criteria Expertness, Reinforcement Learning, Knowledge Transfer.
