Combining Planning with Gaze for Online Human Intention Recognition

Intention recognition is the process of using behavioural cues to infer an agent's goals or future behaviour. People use many behavioural cues to infer others' intentions, such as deliberative actions, facial expressions, eye gaze, and gestures. In artificial intelligence, two approaches for intention recognition, among others, are gaze-based and model-based intention recognition. Approaches in the former class use gaze to determine which parts of a space a person looks at more often to infer a person's intention. Approaches in the latter use models of possible future behaviour to rate intentions as more likely if they are a better 'fit' to observed actions. In this paper, we propose a novel model of human intention recognition that combines gaze and model-based approaches for online human intention recognition. Gaze data is used to build probability distributions over a set of possible intentions, which are then used as priors in a model-based intention recognition algorithm. In humanbehavioural experiments (n = 20) involving a multi-player board game, we found that adding gaze-based priors to model-based intention recognition more accurately determined intentions (p < 0.01), determined those intentions earlier (p < 0.01), and at no additional cost; all compared to a model-based-only approach.