Computational studies to understand the role of social learning in team familiarity and its effects on team performance

Singh, Vishal; Dong, Andy; Gero, John S.

doi:10.1080/15710882.2011.633088

Cited by 18 publications

(23 citation statements)

References 53 publications

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“…explored the effect of team structure and task complexity on the formation of transactive memory (Singh, Dong, & Gero, 2012. That work also obtained results that agreed qualitatively with the literature, but modeled the design problem as an abstract task network instead of directly solving a concrete design problem.…”

supporting

confidence: 74%

“…That work obtained results that agreed qualitatively with the literature, but only explored one-and two-dimensional continuous problem domains, and was not compared to the results of any human studies. A recent agent-based design team model also explored the effect of team structure and task complexity on the formation of transactive memory (Singh, Dong, & Gero, 2012. That work also obtained results that agreed qualitatively with the literature, but modeled the design problem as an abstract task network instead of directly solving a concrete design problem.…”

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confidence: 70%

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Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

2015

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Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena, thus balancing simplicity and direct applicability. This paper discusses the model's composition, and demonstrates its utility through simulating human design teams in a cognitive study. Simulation results are compared directly to the results from human designers. The CISAT model is also used to identify the most beneficial characteristics in the cognitive study. Keywords: computational model, design cognition, teamwork, engineering design Much cognitive research in engineering design has focused on individuals, despite the fact that most engineering design is actually performed by teams (Paulus, Dzindolet, & Kohn, 2011). This work focuses on developing a better understanding of team-based design through computationally simulating the team design process. Empirical studies are a common means for exploring design cognition and for testing new design methodologies. However, these studies can incur a high personnel cost while only returning a limited amount of data. It can also be difficult to isolate the effects of specific characteristics. This work introduces a computational framework that simulates teambased engineering design through creating software agents that directly solve engineering problems. In addition to offering a resource efficient test bed for evaluating design strategies, this framework can be used to test the conclusions from cognitive studies. It can be used to peel apart aspects of human design, and provides a succinct representation of designer behavior. The purpose of the framework is not to replace cognitive studies, but rather to augment traditional methods of investigation, accelerating the discovery of improved design methodologies.A significant amount of work has attempted to simulate the performance of both teams and individuals (Fan & Yen, 2004). For instance, both the Virtual Design Team model, and another model applied to teams at NASA's Jet Propulsion Laboratory, incorporate detailed descriptions of design team organization and interaction (Jin & Levit, 1996;Olson, Cagan, & Kotovsky, 2009). Both models were used to simulate complex design tasks, but were also burdened by high model complexity. For instance, the model by Olson et al. (2009) used approximately 1000 distinct variables, and required nearly 100000 lines of code for implementation. Still other work has utilized agent-based models to explore the formation of mental models during team problem-solving with respect to both interaction structure (Dionne, Sayama, Hao, & James, 2010) and agent memory (Sayama, Farrell, & Dionne, 2011). Mental ...

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confidence: 74%

supporting

confidence: 70%

Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

2015

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“…Although the focus of this work is the application of CISAT, there has been extensive use of agents to model design teams. These include process modeling [11,12] to simulate complex design tasks, mental modeling during team problem-solving with respect to both interaction structure [13] and agent memory [14], exploration of the effect of team structure and task complexity on the formation of transactive memory [15,16], improve managerial planning in product development [17], analyze adaptive team behavior in response to disruptions [18] and generative design via agent modeling [19,20].…”

Section: Agent-based Modeling Of Design Teams Using Cisatmentioning

confidence: 99%

Design Strategy Transfer in Cognitively-Inspired Agents

Raina

McComb

Cagan

2018

Volume 2A: 44th Design Automation Conference

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Planning and strategizing are essential parts of the design process and are based on the designer's skill. Further, planning is an abstract skill that can be transferred between similar problems. However, planning and strategy transfer within design have not been effectively modeled within computational agents. This paper presents an approach to represent this strategizing behavior using a probabilistic model. This model is employed to select the operations that computational agents should perform while solving configuration design tasks. This work also demonstrates that this probabilistic model can be used to transfer strategies from human data to computational agents in a way that is general and useful. This study shows a successful transfer of design strategy from human-to-computer agents, opening up the possibility of deriving high-performing behavior from designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents while transferring design strategies across different problems, improving agent performance significantly. The work presented in this study leverages a computational framework built by embedding cognitive characteristics into agents, which has shown to mimic human problem-solving in configuration design problems.

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“…The amount of information increases more than the square of the number of leaners rather the additively, which requires a robust system which can handle the data. Singh, Dong, & Gero, 2012;Salas, Burke, Fowlkes, & Priest, 2004;Johnston, Serfaty, & Freeman, 2003). Several aspects such as team structure and skillset are important to consider as when training teams it is necessary to train for team skills in addition to task skills.…”

Section: Requirements For a Team Itsmentioning

confidence: 99%

The Challenges of Building Intelligent Tutoring Systems for Teams

Bonner¹,

Gilbert²,

Dorneich³

et al. 2016

Proceedings of the Human Factors and Ergonomics Society Annual

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Intelligent Tutoring Systems have been useful for individual instruction and training, but have not been widely created for teams, despite the widespread use of team training and learning in groups. This paper reviews two projects that developed team tutors: the Team Multiple Errands Task (TMET) and the Recon Task developed using the Generalized Intelligent Framework for Tutoring (GIFT). Specifically, this paper 1) analyzes why team tasks have significantly more complexity than an individual task, 2) describes the two team-based platforms for team research, and 3) explores the complexities of team tutor authoring. Results include a recommended process for authoring a team intelligent tutoring system based on our lessons learned that highlights the differences between tutors for individuals and team tutors.

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Computational studies to understand the role of social learning in team familiarity and its effects on team performance

Cited by 18 publications

References 53 publications

Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model

Design Strategy Transfer in Cognitively-Inspired Agents

The Challenges of Building Intelligent Tutoring Systems for Teams

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