Learning Causality and Intention in Human Actions

Previous research has shown that human actions can be detected by motion patterns. However, labeling motion patterns is not sufficient in a cognitive system that requires reasoning about the agent´s intentions, and how the environmental context affects the way an action is performed. In this paper, we develop a graphical model that captures how the movements that realize the action vary depending on the situations, and present statistical learning algorithms. Using object manipulation tasks, we illustrate how a system infers the agent´s goals from visual observation and compare results with findings in psychological experiments