The problem of probabilistic goal recognition consists of automatically inferring a probability distribution over a range of possible goals of an autonomous agent based on the observations of its behavior. The state-of-the-art approaches for probabilistic goal recognition assume the full knowledge about the world the agent operates in and possible agent's operations in this world. In this paper, we propose a framework for solving the probabilistic goal recognition problem using process mining techniques for discovering models that describe the observed behavior and diagnosing deviations between the discovered models and observations. The framework imitates the principles of observational learning, one of the core mechanisms of social learning exhibited by humans, and relaxes the above assumptions. It has been implemented in a publicly available tool. The reported experimental results confirm the effectiveness and efficiency of the approach, both for rational and irrational agents' behaviors
