Employing models of human social motor behavior for artificial agent trainers

Lillian M. Rigoli*, Patrick Nalepka, Hannah Douglas, Rachel W. Kallen, Simon Hosking, Christopher Best, Elliot Saltzman, Michael Richardson

*Corresponding author for this work

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

Abstract

Many everyday tasks require individuals to work together as a team to achieve a task goal. For many complex or high-stakes multi-agent activities, team members are required to participate in simulated training exercises to develop the task-and team-work (coordination) skills needed to maximize task performance. Such training, however, can be both time-and labor-intensive, requiring the participation of full teams and expert instructors. One way to minimize these costs is to augment team training scenarios with interactive artificial agents (AAs) capable of robust,‘human-like’behavioral interaction. With regard to perceptual-motor tasks specifically, recent research suggests that this can be achieved using task dynamical models derived from the dynamical primitives of human motor behavior. To investigate the degree to which such models can be employed for human team training, we examined whether a task dynamical model of human herding behavior could be embedded in the control architecture of an AA to train novice human-actors to learn various simulated multi-agent herding tasks. Three experiments were conducted that (i) first modeled human team performance during a set of novel herding tasks adapted from [19, 21],(ii) tested an AA utilizing this model to complete the tasks with human novices, and (iii) demonstrated how this AA could effectively train novices in a manner comparable to a human-expert trainer.
Original languageEnglish
Title of host publicationAAMAS '20
Subtitle of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems
EditorsBo An, Neil Yorke-Smith, Amal El Fallah Seghrouchni, Gita Sukthankar
Place of PublicationRichland, SC
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1134-1142
Number of pages9
ISBN (Print)9781450375184
Publication statusPublished - 2020
EventInternational Conference on Autonomous Agents and MultiAgent Systems (19th : 2020) - Auckland, New Zealand
Duration: 9 May 202013 May 2020

Conference

ConferenceInternational Conference on Autonomous Agents and MultiAgent Systems (19th : 2020)
Abbreviated titleAAMAS’20
CountryNew Zealand
CityAuckland
Period9/05/2013/05/20

Keywords

  • agents competing and cooperating with humans
  • agents for improving human cooperative activities
  • human-robot/agent interaction
  • social agent models

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