Classifying intelligence in machines : a taxonomy of intelligent control

Wilson, Callum and Marchetti, Francesco and Di Carlo, Marilena and Riccardi, Annalisa and Minisci, Edmondo (2020) Classifying intelligence in machines : a taxonomy of intelligent control. Robotics, 9 (3). 64. ISSN 2218-6581

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    Abstract

    The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent.

    Creators(s): Wilson, Callum ORCID logoORCID: https://orcid.org/0000-0003-3736-1355, Marchetti, Francesco ORCID logoORCID: https://orcid.org/0000-0003-4552-0467, Di Carlo, Marilena ORCID logoORCID: https://orcid.org/0000-0001-5046-3028, Riccardi, Annalisa ORCID logoORCID: https://orcid.org/0000-0001-5305-9450 and Minisci, Edmondo ORCID logoORCID: https://orcid.org/0000-0001-9951-8528;
    Item type:Article
    ID code:73672
    Keywords:intelligent control, taxonomy, robotics, neural network control, fuzzy logic control, evolutionary algorithms, artificial intelligence, Mechanical engineering and machinery, Mechanical Engineering
    Subjects:Technology > Mechanical engineering and machinery
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Strategic Research Themes > Ocean, Air and Space
    Strategic Research Themes > Measurement Science and Enabling Technologies
    Depositing user: Pure Administrator
    Date deposited:20 Aug 2020 13:56
    Last modified:16 Jan 2021 01:51
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/73672
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