A bio-inspired evolutionary approach to identifying minimal length decision rules in emotional usability engineering

Ikno Kim, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Many of the applied methods and measurement tools of emotional usability engineering have been recommended for use designing products. A rough set method can also be a useful tool to be integrated with the basic concepts of emotional usability engineering. If such a method is applied, the groups of sensory words have to be investigated and their values reduced and classified to provide comprehensive information to product designers. However, a computational problem exists regarding the number of samples, groups of sensory words, and values required when resolving sense-based minimal decision rules. Considering this problem, we discuss the use of DNA computing, and propose a bio-inspired evolutionary method based on the rough set method, which should provide a new tool for emotional usability engineering.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages181-187
    Number of pages7
    Volume5712 LNAI
    EditionPART 2
    DOIs
    Publication statusPublished - 2009
    Event13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009 - Santiago
    Duration: 2009 Sep 282009 Sep 30

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume5712 LNAI
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009
    CitySantiago
    Period09/9/2809/9/30

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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