Combining gaze and AI planning for online human intention recognition

Intention recognition is the process of using behavioural cues, such as deliberative actions, eye gaze, and gestures, to infer an agent's goals or future behaviour. In artificial intelligence, one approach for intention recognition is to use a model of possible behaviour to rate intentions as more likely if they are a better ‘fit’ to actions observed so far. In this paper, we draw from literature linking gaze and visual attention, and we propose a novel model of online human intention recognition that combines gaze and model-based AI planning to build probability distributions over a set of possible intentions. In human-behavioural experiments (n=40) involving a multi-player board game, we demonstrate that adding gaze-based priors to model-based intention recognition improved the accuracy of intention recognition by 22% (p<0.05), determined those intentions ≈90 seconds earlier (p<0.05), and at no additional computational cost. We also demonstrate that, when evaluated in the presence of semi-rational or deceptive gaze behaviours, the proposed model is significantly more accurate (9% improvement) (p<0.05) compared to a model-based or gaze only approaches. Our results indicate that the proposed model could be used to design novel human-agent interactions in cases when we are unsure whether a person is honest, deceitful, or semi-rational.