Probabilistic Human Intention Modeling for Cognitive Augmentation

The aim of cognitive augmentation is to expand the intrinsically limited human's cognitive abilities caused by cognitive impairment or disability. In order to assist the human's limited cognitive ability, we are trying to develop a human augmented cognition system that aims to provide the appropriate information actively corresponding to what user intents to do. In this paper, we mainly address the probabilistic human intention modeling for cognitive augmentation, and its overall process. The types of implicit intention such as navigational and informational intention can be predicted by using fixation count and length induced by eyeball movement. Also, the gradient of pupil size variation is used to detect the transition point between navigational intent and the informational intent. A Naive Bayes classifier is used as a tool for the extraction of query keywords to search and retrieve specific information from personalized knowledge database according to the successive series of attended objects according to a specific informational intent in a situation. The experimental results show that the probabilistic human intention model is suitable for achieving the ultimate purpose of the cognitive augmentation.