There are several benefits provided by Multi-Agent Systems (MAS). Through parallel computing, agents can work together to better explore the decentralized structure of a given task and speed up its completion. In addition, agents can also exchange knowledge through communication, provide scalability by adding new agents when appropriate, and replace troubled agents in cases of failures. A great number of existing agent models is based on reinforcement learning algorithms for learning. When the agent works in small or discrete environments, the results obtained with methods such as Qlearning are satisfactory. However, when the environment is large or continuous reinforcement learning methods become unfeasible due to the large state space. In MAS, this problem is considerably greater, since the required memory begins to grow exponentially with the number of agents involved in the application. The main objective of this thesis is to develop a new model of autonomous learning for multi-agents in order to overcome these limitations. The study consisted of three main stages: literature review, new model development and implementation, and case studies. Literature review included the study of intelligent agents and Multi-Agent Systems, seeking to identify the properties and limitations of the algorithms already developed, existing applications, and desired features in the new MAS. The choice of a neuro-fuzzy hierarchical model of the family RL-NFH as a basis was especially motivated by the importance of extending the autonomy and learning of the agents through intelligence. And also, because of its capacity to overcome some of the limitations present in traditional reinforcement learning algorithms. Initially, the concepts of satisficing and non-domination were incorporated into the previous model to accelerate the learning algorithm. Then, the new multi-agent model was elaborated and implemented, enabling the development of cooperative and competitive applications, with multiple agents. Case studies have covered different situations of cooperation and competition between autonomous agents. Three applications were considered: the Pursuit-Game benckmark game, an electricity auction, where energy suppliers make offers to meet forecast demand in a given period of time, and an application in project management area, where intelligent agents are created to provide activity duration estimates and to automate some processes done usually by the Project Manager. In all case studies, results were compared with conventional techniques and/or the performance of other MAS. The results achieved by the new model are encouraging. The tests showed that the new system has the capacity to coordinate actions between fully autonomous agents in different situations and environments. Moreover, the new model is strongly generic and flexible. Due to these properties, it can be used in future in several other applications involving multiple agents.