Interaction flexibility in artificial agents teaming with humans

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

Abstract

Team interaction involves the division of labor and coordination of actions between members to achieve a shared goal. Although the dynamics of interactions that afford effective coordination and performance have been a focus in the cognitive science community, less is known about how to generate these flexible and adaptable coordination patterns. This is important when the goal is to design artificial agents that can augment and enhance team coordination as synthetic teammates. Although previous research has demonstrated the negative impact of model-based agents on the pattern of interactions between members using recurrence quantification methods, more recent work utilizing deep reinforcement learning has demonstrated a promising approach to bootstrap the design of agents to team with humans effectively. This paper explores the impact of artificial agent design on the interaction patterns that are exhibited in human-autonomous agent teams and discusses future directions that can facilitate the design of human-compatible artificial agents.
Original languageEnglish
Title of host publicationCogSci 2021: program for the 43rd Annual Meeting of the Cognitive Science Society
Subtitle of host publicationcomparative cognition, animal minds
Place of PublicationSeattle, WA
PublisherCognitive Science Society
Pages112-118
Number of pages7
Publication statusPublished - 2021
EventAnnual Meeting of the Cognitive Science Society (43rd : 2021) - Vienna, Austria
Duration: 26 Jul 202129 Jul 2021

Conference

ConferenceAnnual Meeting of the Cognitive Science Society (43rd : 2021)
Abbreviated titleCogSci 2021
Country/TerritoryAustria
CityVienna
Period26/07/2129/07/21

Keywords

  • human-autonomy teaming (HAT)
  • deep reinforcement learning (DRL)
  • interactive team cognition (ITC)
  • recurrence quantification analysis (RQA)

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