PROBABILISTIC INTENTION CLASSIFICATION FOR HUMAN AUGMENTED COGNITION SYSTEM

Byunghun Hwang, Young-Min Jang, Minho Lee

Abstract

In this paper, we present a probabilistic human implicit intention classification using user’s eye gaze data for human augmented cognition system. The Ultimate purpose of this method is to implement a human augmented cognition system which can provide a specific service to address the cognitive limitations of human brain. In order to partially overcome the cognitive limitations, the system should be able to control the flow of information. Therefore, a specific intention classification using a Naïve Bayes classifier can be used as useful tool for searching and retrieving specific information according to the human intention and situation.

Keywords

human intention, Naïve Bayes, human augmented cognition, system architecture

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IMEKO-WC-2012-TC18-P4.pdf

DOI

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IMEKO TC

TC18 - Measurement of Human Functions

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