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Poster Communications Year : 2017

A Bayesian Experimental Design Approach Maximizing Information Gain for Human-Computer Interaction

Abstract

A new information-theoretic approach based on Bayesian Experimental Design (BED) is applied to human-computer interaction, and in particular to multi-scale navigation. Instead of simply executing user commands, our BIG (Bayesian Information Gain) technique is modeling user behavior and tries to gain information by maximizing the expected mutual information provided by the users' subsequent input.
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Dates and versions

hal-01677034 , version 1 (07-01-2018)

Identifiers

  • HAL Id : hal-01677034 , version 1

Cite

Wanyu Liu, Rafael Lucas d'Oliveira, Michel Beaudouin-Lafon, Olivier Rioul. A Bayesian Experimental Design Approach Maximizing Information Gain for Human-Computer Interaction. ITA 2017 - IEEE Information Theory and Applications Workshop, Feb 2017, San Diego, United States. , pp.1. ⟨hal-01677034⟩
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