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Cognitive Computation

, Volume 4, Issue 2, pp 157–171 | Cite as

CO-WORKER: Toward Real-Time and Context-Aware Systems for Human Collaborative Knowledge Building

  • Stefano Squartini
  • Anna EspositoEmail author
Article

Abstract

The information exchange occurring during human interactions, conveyed through verbal and nonverbal communication modes, builds up a new-shared knowledge among the interacting people. A current automatic meeting assistance system is just able to store such an exchange (for successive offline processing), while it would be valuable developing automatic tools that provide appropriate support as it takes place. Currently, the international scientific community is strongly committed toward the implementation of intelligent instruments able to recognize and process in real-time relevant interactional signals in order to provide timely support to the happening interaction. This work will argue on an even more comprehensive paradigm for collaborative computer support to human interaction, not adequately addressed in the literature so far, concerning the implementation of human–computer interaction (HCI) systems able to process in real-time multimodal signals, to infer contextual information, and support in a collaborative way human interaction in-group activities, such as learning, discussion, work cooperation, decision-making, and problem solving. Such systems should act as co-workers, actively cooperating and contributing to the group’s knowledge building and pretending to share with the group, significances and individual potentialities rather than act as passive data storing devices. In carrying out their functions, these HCI systems will be placed on a group cognitive level, where individual purposes, actions, and emotions are mediated by the group interaction, and meanings are mainly built through the group shared knowledge and experience.

Keywords

Collaborative knowledge building (CKB) Human–computer interaction (HCI) Real-time signal processing Affective computing Group cognition Machine learning 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.3MediaLabs, Dipartimento di Ingegneria dell’InformazioneUniversità Politecnica delle MarcheAnconaItaly
  2. 2.Dipartimento di Psicologia and IIASSSeconda Università di NapoliSalernoItaly

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