Developing Effective and Resilient Human-Agent Teamwork Using Team Design Patterns

Diggelen, J. van; Neerincx, Mark A.; Peeters, Marieke; Schraagen, Jan Maarten

doi:10.1109/mis.2018.2886671

Cited by 26 publications

(22 citation statements)

References 17 publications

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“…The second aim is to evaluate the generalizability to other teams of the differences in communication patterns found so far with a limited number and types of teams (David & Schraagen, 2018). In line with Woods and Hollnagel’s (2006) notion of “patterns,” teams from different domains are expected to adapt to critical situations in similar ways and thus change communication patterns in similar ways (see also van Diggelen et al, 2018). As such, studies applying REM to other teams than cockpit teams may find similar differences in communication patterns when those teams encounter critical situations.…”

Section: Research Aims and Outlinementioning

confidence: 94%

Team Communication Patterns in Critical Situations

Oever

Schraagen

2021

Journal of Cognitive Engineering and Decision Making

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Teams operating in time-pressured, dynamic environments frequently need to cope with critical situations varying in complexity and hazard. To cope with critical situations, teams may have to adapt their communication processes. Adaptation of team communication processes has been studied mostly at short time frames (minutes). Literature on adapting communication at longer time frames is limited (hours, relative to minutes). We used the relational event model to compare team communication in critical and noncritical situations of pediatric cardiac surgeries and Apollo 13 flight director’s voice loops. Teams showed some flattening of communication structures in critical situations. Both teams maintained institutional roles and displayed closed-loop and information-seeking communication. Communication patterns may change further with increasing criticality. The exact way teams adapt to critical situations may differ depending on team, team size and situation. Findings may inform team training procedures or team structure development.

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Section: Research Aims and Outlinementioning

confidence: 94%

Team Communication Patterns in Critical Situations

Oever

Schraagen

2021

Journal of Cognitive Engineering and Decision Making

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“…Within HAT research, and related fields such as humancomputer interaction, problem solutions are often formulated using design patterns (Kruschitz and Martin, 2010;Schulte et al, 2016;Diggelen et al, 2018). A design pattern is an evaluated and abstracted solution for a common problem (Alexander, 1977).…”

Section: Team Design Patterns For Human-agent Collaborationmentioning

confidence: 99%

“…We adopt the approach to define human-agent collaboration as standardized sequences of interactions, as proven solutions to commonly recurring issues in team tasks. These are called Team Design Patterns (TDPs) (Diggelen et al, 2018), and they define the interactions and collaborations within the team (e.g., task division; autonomy; authorities and mandates; communication). Based on the work of van der , we select three TDPs for human-agent team collaboration (see Section 4, and use these for our exploration into their effects on MHC.…”

Section: Introductionmentioning

confidence: 99%

Moral Decision Making in Human-Agent Teams: Human Control and the Role of Explanations

Waa

Verdult²,

Bosch³

et al. 2021

Front. Robot. AI

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With the progress of Artificial Intelligence, intelligent agents are increasingly being deployed in tasks for which ethical guidelines and moral values apply. As artificial agents do not have a legal position, humans should be held accountable if actions do not comply, implying humans need to exercise control. This is often labeled as Meaningful Human Control (MHC). In this paper, achieving MHC is addressed as a design problem, defining the collaboration between humans and agents. We propose three possible team designs (Team Design Patterns), varying in the level of autonomy on the agent’s part. The team designs include explanations given by the agent to clarify its reasoning and decision-making. The designs were implemented in a simulation of a medical triage task, to be executed by a domain expert and an artificial agent. The triage task simulates making decisions under time pressure, with too few resources available to comply with all medical guidelines all the time, hence involving moral choices. Domain experts (i.e., health care professionals) participated in the present study. One goal was to assess the ecological relevance of the simulation. Secondly, to explore the control that the human has over the agent to warrant moral compliant behavior in each proposed team design. Thirdly, to evaluate the role of agent explanations on the human’s understanding in the agent’s reasoning. Results showed that the experts overall found the task a believable simulation of what might occur in reality. Domain experts experienced control over the team’s moral compliance when consequences were quickly noticeable. When instead the consequences emerged much later, the experts experienced less control and felt less responsible. Possibly due to the experienced time pressure implemented in the task or over trust in the agent, the experts did not use explanations much during the task; when asked afterwards they however considered these to be useful. It is concluded that a team design should emphasize and support the human to develop a sense of responsibility for the agent’s behavior and for the team’s decisions. The design should include explanations that fit with the assigned team roles as well as the human cognitive state.

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“…Despite the intricacies involved, we can observe patterns in team behavior which allow us to describe at a general level how AI systems are to collaborate with humans [18,25]. A team design pattern (TDP) is defined as a description of generic reusable behaviors of actors for supporting effective and resilient teamwork [30]. In [29], a simple graphical language is defined to describe team patterns, providing an intuitive way to facilitate discussions about human-machine teamwork solutions among a wide range of stakeholders including non-experts.…”

Section: Human-agent Teaming and Design Patternsmentioning

confidence: 99%

Allocation of Moral Decision-Making in Human-Agent Teams: A Pattern Approach

Waa

Diggelen²,

Siebert

et al. 2020

Engineering Psychology and Cognitive Ergonomics. Cognition and Design

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Artificially intelligent agents will deal with more morally sensitive situations as the field of AI progresses. Research efforts are made to regulate, design and build Artificial Moral Agents (AMAs) capable of making moral decisions. This research is highly multidisciplinary with each their own jargon and vision, and so far it is unclear whether a fully autonomous AMA can be achieved. To specify currently available solutions and structure an accessible discussion around them, we propose to apply Team Design Patterns (TDPs). The language of TDPs describe (visually, textually and formally) a dynamic allocation of tasks for moral decision making in a human-agent team context. A task decomposition is proposed on moral decision-making and AMA capabilities to help define such TDPs. Four TDPs are given as examples to illustrate the versatility of the approach. Two problem scenarios (surgical robots and drone surveillance) are used to illustrate these patterns. Finally, we discuss in detail the advantages and disadvantages of a TDP approach to moral decision making.

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Developing Effective and Resilient Human-Agent Teamwork Using Team Design Patterns

Cited by 26 publications

References 17 publications

Team Communication Patterns in Critical Situations

Team Communication Patterns in Critical Situations

Moral Decision Making in Human-Agent Teams: Human Control and the Role of Explanations

Allocation of Moral Decision-Making in Human-Agent Teams: A Pattern Approach

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