Evaluating human-artificial agent decision congruence in a coordinated action task

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Recommender systems designed to augment human decision-making in multi-agent tasks need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. Further, if these systems are to be used for skill training, they need to impart implicit learning to its users. This work compared a recommender system trained using deep reinforcement learning to a heuristic-based system in recommending actions to human participants teaming with an artificial agent during a collaborative problem-solving task. In addition to evaluating task performance and learning, we also evaluate the extent to which the human action are congruent with the recommended actions.

Original languageEnglish
Title of host publicationHAI 2022
Subtitle of host publicationProceedings of the 10th Conference on Human-Agent Interaction
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages327-329
Number of pages3
ISBN (Electronic)9781450393232
DOIs
Publication statusPublished - 5 Dec 2022
EventInternational Conference on Human-Agent Interaction (10th : 2022) - Christchurch, New Zealand
Duration: 5 Dec 20228 Dec 2022

Conference

ConferenceInternational Conference on Human-Agent Interaction (10th : 2022)
Abbreviated titleHAI ’22
Country/TerritoryNew Zealand
CityChristchurch
Period5/12/228/12/22

Keywords

  • decision making
  • hierarchical deep reinforcement learning
  • multi-agent coordination
  • recommender system
  • shepherding

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