Modelling competitive human action using dynamical motor primitives for the development of human-like artificial agents

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Abstract

With artificial intelligence technologies becoming commonplace today, enhancing the efficiency of human-artificial agent (AA) interactions has become increasingly important. A growing body of research has revealed how dynamic motor primitives (DMPs) of human perceptual-motor behavior can be used to create ‘human-like’ AAs, primarily focusing on cooperative tasks. Using air hockey as a representative task, the current experiment is the first part of a large study aimed at determining the utility of DMP-based models for developing ‘human-like’ competitive AAs. Participants played against a preliminary DMP model and the differences in their behaviors were analyzed. Based on these observed differences, a revised model is proposed, with preliminary results revealing that the new model exhibits behaviors more consistent with those of humans. A major implication of this work is that it presents a framework for creating ‘human-like’ AAs that capture the essential human decision and movement dynamics without requiring large human gameplay datasets.
Original languageEnglish
Title of host publicationCogSci2022
Subtitle of host publicationproceedings of the 44th Annual Conference of the Cognitive Science Society
EditorsJennifer Culbertson, Andrew Perfors, Hugh Rabagliati, Veronica Ramenzoni
Place of PublicationAustin, Texas
PublisherCognitive Science Society
Pages2731-2737
Number of pages7
Publication statusPublished - 2022
EventAnnual Meeting of the Cognitive Science Society (44th : 2022) - Toronto, Canada
Duration: 27 Jul 202230 Jul 2022

Publication series

NameProceedings of the Annual Meeting of the Cognitive Science Society
Volume44
ISSN (Electronic)1069-7977

Conference

ConferenceAnnual Meeting of the Cognitive Science Society (44th : 2022)
Abbreviated titleCogSci 2022
Country/TerritoryCanada
CityToronto
Period27/07/2230/07/22

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • dynamic motor primitives
  • human-machine interaction
  • human behavior modelling
  • artificial intelligence

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