This study develops a team classification scheme for human-agent teaming (HAT) and, based on this, analyzes 25 testbeds utilized in 68 empirical studies on HAT. The team classification scheme, adapting an existing scheme used for human-human teams, consists of nine dimensions, including team composition, task interdependence, role structure, leadership structure, authority differentiation, communication structure, communication direction, communication medium, and team life span. This scheme was then applied to analyze 25 testbeds utilized in 68 empirical studies. We found that a significant portion of existing literature on HAT focused on teams consisting of one human and one agent, with humans typically assuming leadership roles. Moreover, the dynamics within these teams tended to remain static over time. Our findings highlight the importance of further research into diverse team attributes, such as team composition, leadership structure, and communication structure. Such efforts would facilitate a deeper understanding of complex team dynamics in human-agent teaming.
Task Interdependence in Human-Robot Teaming
Human-robot teaming is becoming increasingly common within manufacturing processes. A key aspect practitioners need to decide on when developing effective processes is the level of task interdependence between human and robot team members. Task interdependence refers to the extent to which one’s behavior affects the performance of others in a team. In this work, we examine the effects of three levels of task interdependence—pooled, sequential, reciprocalin human-robot teaming on human worker’s mental states, task performance, and perceptions of the robot. Participants worked with the robot in an assembly task while their heart rate variability was being recorded. Results suggested human workers in the reciprocal interdependence level experienced less stress and perceived the robot more as a collaborator than other two levels. Task interdependence did not affect perceived safety. Our findings highlight the importance of considering task structure in human-robot teaming and inform future research on and industry practices for human-robot task allocation.
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- Award ID(s):
- 1822872
- PAR ID:
- 10176315
- Date Published:
- Journal Name:
- IEEE International Conference on Robot and Human Interactive Communication
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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