Interactive Team Cognition

Cooke, Nancy J.; Gorman, Jamie C.; Myers, Christopher W.; Duran, Jasmine L.

doi:10.1111/cogs.12009

Cited by 370 publications

(320 citation statements)

References 83 publications

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“…As work has become increasingly cognitive and complex, the key factor for today′s organizations to gain a competitive advantage is often the knowledge that organizational members possess. However, as tasks are increasingly cognitive and complex, they often exceed an individual′s cognitive ability to have complete awareness of all the perspectives of a given task or situation (Cooke et al, 2013). Teamwork is considered to be an effective solution to this contradiction, since working in teams ensures that members can share this cognitive workload and contribute their expertise to different parts of the task (Mathieu et al, 2000;Hámornik and Juhász, 2010), and are thus able to cope with complex situations.…”

Section: Definition Of Teams and Team Knowledgementioning

confidence: 99%

Adaptation and Cognition of High-Risk Environment Teams in an Input-Mediator-Outcome Framework

Takács

Juhász

2018

Period. Polytech. Soc. Man. Sci.

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The aim of this article is to introduce a conceptual, integrative model of the adaptation of teams in high risk environments. The model is a combination of previous theoretical frameworks of adaptation and task execution, with the aim of providing a comprehensive model for understanding team adaptation specific to high risk environments. We give equal importance to adaptation as an input, a mediator, and an outcome by putting it in an Input-Mediator-Outcome model, although we further wish to emphasize the relevance of team cognition in team adaptation. In addition, we aim to highlight that, depending on the trigger and the already existing characteristics of the team, adaptation might either follow an algorithm-based or a knowledge-based pattern.

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Section: Definition Of Teams and Team Knowledgementioning

confidence: 99%

Adaptation and Cognition of High-Risk Environment Teams in an Input-Mediator-Outcome Framework

Takács

Juhász

2018

Period. Polytech. Soc. Man. Sci.

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“…Importantly, GSC loosens these strict boundaries by mixing the human and robot distributions. We note that cognitive systems engineering [11], [16], [33], [15], team cognition theory [12], [40], [19], [13], [12] and human-agentrobot teamwork [7], [8] make important conceptual progress towards understanding such a nuanced human-machine negotiation. However, these approaches lack the mathematical precision, performance guarantees, and computational tractability provided by sparse-GSC.…”

Section: Related Workmentioning

confidence: 99%

“…Typically, this question is resolved using heuristic arguments: follow an LOA/FA approach, such as "the human retains control under these circumstances, and the machine retains control under other circumstances". The LOA/FA paradigm is insufficiently nuanced; we search instead for a mathematical approach inspired by the teaming literature [16], [12], [7]. To do so, we show how the question-h * or f R * ?-is resolved by requiring that p(h, f R | z 1:t ) respects the intent and flexibility information contained in p(h | z h 1:t ) and p(f R | z R 1:t ).…”

Section: B Shared Control As a Joint Decision Making Problemmentioning

confidence: 99%

Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

Trautman

2017

Robotics: Science and Systems XIII

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Abstract-Human machine teaming has, for decades, been conceptualized as a function allocation (FA) or levels of autonomy (LOA) process: the human is suited for some tasks, while the machine is suitable for others, and as machines improve they take over duties previously assigned to humans. A wide variety of methods-including adaptive, adjustable, blended, supervisory and mixed initiative control, implemented discretely or continuously, as potential fields, as virtual fixture interfaces, or haptic interfaces-are derivatives of FA/LOA. We formalize FA/LOA (and all their derivatives) under a single mathematical formulation called classical shared control (CSC). Despite the widespread adoption of CSC, we prove that it fails to optimize human and robot agreement and intent if either the human or robot model displays "intention ambiguity" (e.g., the human's intended goal is unclear or the robot finds multiple viable solutions). Practically, this suboptimality can manifest as unnecessary and unresolvable disagreement (an unnecessary deadlock). For instance, if the robot chooses to go left around an obstacle and the human chooses to go right, CSC only provides two solutions: freeze in place or collide with the obstacle (we provide a wide variety of failure examples in [52], https://arxiv.org/abs/1611.09490). We find that CSC suboptimality stems from arbitrating over model samples, rather than over models. Our key insight is thus to arbitrate over human and robot distributions; we prove this method optimizes human and robot agreement and intent and resolves deadlocking. Our key contribution is computationally efficient distribution arbitration: if the human and robot carry N our joint has fewer modes than the individual agent models. We call our approach N min -sparse generalized shared control.

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“…They concluded that these models were the Bknowledge structures held by members of a team that enable them to form accurate explanations and expectations for the task, and in turn, to coordinate their actions and adapt their behavior to demands of the task and other team members^(p. 228). More recently Cooke et al (2012) and Gorman (2014) have emphasized that these mental models should not be viewed as property or products, but as dynamic cognitive activities rooted in team member interactions and a meaningful context. Given this understanding of shared mental models as complex, dynamic processes begins to suggest the need for machine intelligence, such as that found in ITSs, to provide adaptive instruction for teams.…”

Section: Shared Mental Models In Teamsmentioning

confidence: 99%

Shared Mental Models in Support of Adaptive Instruction for Teams Using the GIFT Tutoring Architecture

Fletcher

Sottilare

2017

Int J Artif Intell Educ

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Teams and teamwork are ubiquitous in military and civilian organizations. Their importance to organizational success cannot be overstated. This article describes the relationship and effect of three concepts: Intelligent Tutoring Systems (ITSs), shared mental models, and teamwork. The nexus between these concepts is examined to determine its capability to support adaptive instruction of teams, defined here as collectives of interdependent individuals who must communicate and interact with each other in order to perform assigned tasks and missions. An assumption underlying this examination is that augmenting the mental modeling processes of ITS with the mental models shared by members of interdependent teams will allow the considerable and increasingly research-established capabilities of intelligent tutoring of individuals to be applied in training teams. Specifically, we reviewed the learning and performance literature to identify how shared mental models of cognition could be used to enhance the adaptive instruction of teams. Our goal is to develop a methodology to enhance training and educational options for institutions that provide adaptive team instruction at the point-of-need. Toward this end, we discuss the adaptation of the Generalized Intelligent Framework for Tutoring (GIFT), an open source tutoring architecture, to accommodate team models and states. While this article makes a first step toward defining a process for team tutoring, challenges remain. Team tutors must have the ability to manage uncertainty and the dynamic nature of team interaction and communication in order to make effective and timely decisions that optimize team and team member performance.

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Interactive Team Cognition

Cited by 370 publications

References 83 publications

Adaptation and Cognition of High-Risk Environment Teams in an Input-Mediator-Outcome Framework

Adaptation and Cognition of High-Risk Environment Teams in an Input-Mediator-Outcome Framework

Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

Shared Mental Models in Support of Adaptive Instruction for Teams Using the GIFT Tutoring Architecture

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