Paper Info
Title
Combining Planning With Gaze For Online Human Intention Recognition
Abstract
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 human behavioural 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.
Year
DOI
Venue
2018
10.5555/3237383.3237457
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18)
Keywords
Field
DocType
Intention Recognition, Gaze, Planning
Gaze,Computer science,Gesture,Human–computer interaction,Facial expression,Eye tracking,Probability distribution,Artificial intelligence,Recognition algorithm,Prior probability,Machine learning
Conference
Citations 
PageRank 
References 
1
0.35
11
Authors
6
Name
Order
Citations
PageRank
Ronal Singh142.84
Tim Miller214213.81
Joshua Newn36711.06
Liz Sonenberg4802119.89
Eduardo Velloso540032.81
Frank Vetere61805143.63