Human computing and machine understanding of human behavior: A survey

Alex Pentland, Thomas S. Huang

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    161 Citations (Scopus)

    Abstract

    A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, humanlike interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses how far are we from enabling computers to understand human behavior.
    Original languageUndefined
    Title of host publicationArtificial Intelligence for Human Computing
    EditorsTh.S. Huang, Antinus Nijholt, Maja Pantic, A. Pentland
    Place of PublicationLondon
    PublisherSpringer
    Pages47-71
    Number of pages25
    ISBN (Print)978-3-540-72346-2
    DOIs
    Publication statusPublished - 15 Jun 2007

    Publication series

    NameLecture Notes in Artificial Intelligence
    PublisherSpringer Verlag
    Number1, suppl./4451
    Volume4451
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Keywords

    • EWI-10299
    • HMI-MI: MULTIMODAL INTERACTIONS
    • Multimodal DataAnalysis
    • Human Behavior Understanding
    • METIS-241695
    • EC Grant Agreement nr.: FP6/033812
    • IR-61756
    • Affective Computing
    • Human sensing
    • Socially-aware Computing

    Cite this

    Pentland, A., & Huang, T. S. (2007). Human computing and machine understanding of human behavior: A survey. In T. S. Huang, A. Nijholt, M. Pantic, & A. Pentland (Eds.), Artificial Intelligence for Human Computing (pp. 47-71). [10.1007/978-3-540-72348-6_3] (Lecture Notes in Artificial Intelligence; Vol. 4451, No. 1, suppl./4451). London: Springer. https://doi.org/10.1007/978-3-540-72348-6_3