We adopt decision theory as a descriptive paradigm to model rational agents. We use influence diagrams as a modeling representation of agents, which is used to interact with them and to predict their behavior. In this paper, we provide a framework that an agent can use to learn the models of other agents in a multi-agent system (MAS) based on their observed behavior. Since the correct model is usually not known with certainty our agents maintain a number of possible models and assign a probability to each of them being correct. When none of the available models is likely to be correct, we modify one of them to better account for the observed behaviors. The modification refines the parameters of the influence diagram used to model the other agent’s capabilities, preferences, or beliefs. The modified model is then allowed to compete with the other models and the probability assigned to it being correct can be arrived at based on how well it predicts the behaviors of the other agent already observed.