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- Nalepka, Patrick, et al.
(author)
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Human social motor solutions for human-machine interaction in dynamical task contexts
- 2019
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In: Proceedings of the National Academy of Sciences of the United States of America. - : National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 116:4, s. 1437-1446
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Journal article (peer-reviewed)abstract
- Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human–machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Human–human and human–artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human–human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a “Turing-like†methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.
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- Richardson, Michael J., et al.
(author)
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Modeling embedded interpersonal and multiagent coordination
- 2016
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In: COMPLEXIS 2016 - Proceedings of the 1st International Conference on Complex Information Systems. - Setubal : SciTePress. - 9789897581816 ; , s. 155-164
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Conference paper (peer-reviewed)abstract
- Interpersonal or multiagent coordination is a common part of everyday human activity. Identifying the dynamic processes that shape and constrain the complex, time-evolving patterns of multiagent behavioral coordination often requires the development of dynamical models to test hypotheses and motivate future research questions. Here we review a task dynamic framework for modeling multiagent behavior and illustrate the application of this framework using two examples. With an emphasis on synergistic self-organization, we demonstrate how the behavioral coordination that characterizes many social activities emerges naturally from the physical, informational, and biomechanical constraints and couplings that exist between two or more environmentally embedded and mutually responsive individuals.
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