Probabilistic Relational Learning of Human Behavior Models

In this paper we introduce a probabilistic relational framework for automated human behavior modeling. This is achieved by observing a human successfully achieving given goals. The proposed modeling algorithm uses commonsense background knowledge analytically to facilitate modeling of complicated human behavior. At the same time it is grounded in physical observations and takes noisy sensory data as input. The separation of domain axioms and modeling algorithm provides a framework which is easily applicable across domains. The acquired model is represented in the form of probabilistic hierarchical task network which provides flexible models applicable to similar tasks.