The Impact of Perceived Autonomous Agents on Dynamic Team Behaviors

Adaptive complex team behaviors evolve dynamically and occur in many different environments. In this study, we examined the role of these behaviors and their relationship with team performance in the context of human-autonomy teams (HAT) and all-human teams. The HAT served as the “synthetic” condition in which two human team members were informed that the third team member was a “synthetic” agent; in the control condition, the team members were informed that the pilot was a remotely located human teammate. Following are the primary findings from this study: first, control teams demonstrated better performance than the synthetic teams; second, control teams were more active than the synthetic teams in terms of planning the task, and third, the behavioral passiveness of the synthetic teams (due to lack of planning) associated with diminished team performance. This suggests that the synthetic teams did not show enough adaptive complex behaviors that were evident in control teams. This finding implies that merely believing the pilot to be a synthetic agent made it more difficult for synthetic teams to plan and, thus, effectively anticipate their teammates’ needs. In addition, this study highlights that there is a significant need for humans to gain experience in working with a synthetic agent to overcome negative perceptions.

[1]  L. Hedges,et al.  Intraclass Correlation Values for Planning Group-Randomized Trials in Education , 2007 .

[2]  Nancy J. Cooke,et al.  Human Teaming Changes Driven by Expectations of a Synthetic Teammate , 2014 .

[3]  Joshua F. Wiley,et al.  Growth Curve Analysis and Visualization Using R , 2014 .

[4]  M. Losada,et al.  The complex dynamics of high performance teams , 1999 .

[5]  Nancy J. Cooke,et al.  Designing a Synthetic Task Environment , 2017 .

[6]  Kerstin Dautenhahn,et al.  A Paradigm Shift in Artificial Intelligence: Why Social Intelligence Matters in the Design and Development of Robots with Human-Like Intelligence , 2006, 50 Years of Artificial Intelligence.

[7]  J. Spencer,et al.  Defending qualitative change: the view from dynamical systems theory. , 2008, Child development.

[8]  V. Simmering,et al.  Applications of Dynamic Systems Theory to Cognition and Development: New Frontiers. , 2017, Advances in child development and behavior.

[9]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[10]  V. Groom,et al.  Can robots be teammates?: Benchmarks in human–robot teams , 2007 .

[11]  Nancy J. Cooke,et al.  Team Coordination Dynamics in Human-Autonomy Teaming , 2017 .

[12]  Mustafa Demir,et al.  The Impact of Coordination Quality on Coordination Dynamics and Team Performance: When Humans Team with Autonomy , 2017 .

[13]  Nancy J. Cooke,et al.  Team synchrony in human-autonomy teaming , 2017 .

[14]  Nancy J. Cooke,et al.  Interactive Team Cognition , 2013, Cogn. Sci..

[15]  Riccardo Fusaroli,et al.  Investigating Conversational Dynamics: Interactive Alignment, Interpersonal Synergy, and Collective Task Performance , 2016, Cogn. Sci..

[16]  N. Marwan,et al.  Recurrence quantification analysis : theory and best practices , 2015 .

[17]  Kara A. Incalcaterra,et al.  A meta-analysis of team-efficacy, potency, and performance: interdependence and level of analysis as moderators of observed relationships. , 2002, The Journal of applied psychology.

[18]  D. Vijay Rao,et al.  Design and Development of Intelligent Military Training Systems and Wargames , 2016, Recent Advances in Computational Intelligence in Defense and Security.

[19]  Nancy J. Cooke,et al.  Synthetic Teammate Communication and Coordination With Humans , 2015 .

[20]  Nicholas Stergiou,et al.  Nonlinear Analysis for Human Movement Variability , 2016 .

[21]  J. Hackman,et al.  The design of work teams , 1987 .

[22]  Silvia Liverani R: A programming environment for Data Analysis and Graphics , 2008 .

[23]  Gareth R. Jones,et al.  The experience and evolution of trust: Implications for cooperation and teamwork , 1998 .

[24]  Nancy J. Cooke,et al.  Team communication behaviors of the human-automation teaming , 2016, 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[25]  Johanna Helena Kerstholt,et al.  The effect of time pressure on decision-making behaviour in a dynamic task environment , 1994 .

[26]  Nancy J. Cooke,et al.  Team Cognition as Interaction , 2015 .

[27]  Esther Thelen,et al.  Dynamic Systems Theories , 2007 .

[28]  Eduardo Salas,et al.  The Measurement of Team Process , 1995, Hum. Factors.

[29]  U. Krogmann From Automation to Autonomy-Trends Towards Autonomous Combat Systems , 2000 .

[30]  A. Opstal Dynamic Patterns: The Self-Organization of Brain and Behavior , 1995 .

[31]  R. McDaniel,et al.  Case Study Research: The View From Complexity Science , 2005, Qualitative health research.

[32]  Travis J. Wiltshire,et al.  Technology as Teammate: Examining the Role of External Cognition in Support of Team Cognitive Processes , 2016, Front. Psychol..

[33]  Sara A. McComb,et al.  Using recurrence analysis to examine group dynamics. , 2016 .

[34]  Nancy J. Cooke,et al.  The Synthetic Teammate as a Team Player in Command-and-Control Teams , 2016 .

[35]  E. Salas,et al.  Toward an understanding of team performance and training. , 1992 .

[36]  F.E. Cellier,et al.  High-autonomy control of space resource processing plants , 1993, IEEE Control Systems.

[37]  Laura K. Allen,et al.  What'd you say again?: recurrence quantification analysis as a method for analyzing the dynamics of discourse in a reading strategy tutor , 2017, LAK.

[38]  M. Rizzi,et al.  The Early Phases of Epileptogenesis Induced by Status Epilepticus Are Characterized by Persistent Dynamical Regime of Intermittency Type , 2016 .

[39]  Nancy J. Cooke,et al.  Team coordination dynamics. , 2010, Nonlinear dynamics, psychology, and life sciences.

[40]  Nancy J. Cooke,et al.  Acquisition and Retention of Team Coordination in Command-and-Control , 2007 .

[41]  Emily D. Heaphy,et al.  The Role of Positivity and Connectivity in the Performance of Business Teams , 2004 .

[42]  Gregory J. Funke,et al.  Recurrence Quantification Analysis Used to Assess Team Communication in Simulated Air Battle Management , 2012 .

[43]  Jeffrey M. Bradshaw,et al.  Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent Activity , 2004, IEEE Intell. Syst..

[44]  Nancy J. Cooke,et al.  Measuring Patterns in Team Interaction Sequences Using a Discrete Recurrence Approach , 2012, Hum. Factors.

[45]  Moreno I. Coco,et al.  Cross-recurrence quantification analysis of categorical and continuous time series: an R package , 2013, Front. Psychol..

[46]  P. Maes Modeling adaptive autonomous agents , 1993 .

[47]  Nancy J. Cooke,et al.  Team situation awareness within the context of human-autonomy teaming , 2017, Cognitive Systems Research.

[48]  S. Russell,et al.  Physio-behavioral coupling in a cooperative team task: contributors and relations. , 2014, Journal of experimental psychology. Human perception and performance.

[49]  G. Ermentrout Dynamic patterns: The self-organization of brain and behavior , 1997 .

[50]  Stephen M Fiore,et al.  Augmenting team cognition in human-automation teams performing in complex operational environments. , 2007, Aviation, space, and environmental medicine.

[51]  Hussein A. Abbass,et al.  I. Background , 2022 .

[52]  Axel Schulte,et al.  Design Patterns for Human-Cognitive Agent Teaming , 2016, HCI.

[53]  P. Brockhoff,et al.  lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package) , 2014 .