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@article{mccomb2015liftingtheveil:,
abstract = {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 …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Design Studies 40, 119-142, 2015},
journal = {Design Studies},
pages = {119-142},
pub_year = {2015},
publisher = {Elsevier},
title = {Lifting the Veil: Drawing Insights about Design Teams from a Cognitively-inspired Computational Model},
volume = {40}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2015liftingtheveil:",
"abstract": "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 …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Design Studies 40, 119-142, 2015",
"conference": null,
"journal": "Design Studies",
"number": null,
"pages": "119-142",
"pub_year": 2015,
"publisher": "Elsevier",
"title": "Lifting the Veil: Drawing Insights about Design Teams from a Cognitively-inspired Computational Model",
"volume": "40"
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@article{raina2019learningtodesign,
abstract = {Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large-scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem-solving team can be created. This paper presents an approach to extract human design strategies and implicit rules, purely from historical human data, and use that for design generation. A two-step framework that learns to imitate human design strategies from observation is proposed and implemented. This framework makes use of deep learning constructs to learn to generate designs without any explicit information about objective and performance metrics. The framework is designed to interact with the problem through a visual interface as humans did when solving the problem. It is trained to imitate a set of human designers by …},
author = {Ayush Raina and Christopher McComb and Jonathan Cagan},
citation = {Journal of Mechanical Design, 2019},
journal = {Journal of Mechanical Design},
pub_year = {2019},
title = {Learning To Design From Humans: Imitating Human Designers Through Deep Learning}
}
| {
"ENTRYTYPE": "article",
"ID": "raina2019learningtodesign",
"abstract": "Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large-scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem-solving team can be created. This paper presents an approach to extract human design strategies and implicit rules, purely from historical human data, and use that for design generation. A two-step framework that learns to imitate human design strategies from observation is proposed and implemented. This framework makes use of deep learning constructs to learn to generate designs without any explicit information about objective and performance metrics. The framework is designed to interact with the problem through a visual interface as humans did when solving the problem. It is trained to imitate a set of human designers by …",
"author": "Ayush Raina and Christopher McComb and Jonathan Cagan",
"citation": "Journal of Mechanical Design, 2019",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2019,
"publisher": null,
"title": "Learning To Design From Humans: Imitating Human Designers Through Deep Learning",
"volume": null
} | 0P9w_S0AAAAJ:O3NaXMp0MMsC | 112 | /scholar?hl=en&cites=3420341011426968484 | [
"3420341011426968484"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/141/11/111102/955339 | /scholar?oi=bibs&hl=en&q=related:pAPONAd-dy8J:scholar.google.com/ | null | null | null | null | 16 | 20 | 24 | 30 | 21 | [
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@article{zhang2021acautionarytale,
abstract = {Recent advances in artificial intelligence (AI) offer opportunities for integrating AI into human design teams. Although various AIs have been developed to aid engineering design, the impact of AI usage on human design teams has received scant research attention. This research assesses the impact of a deep learning AI on distributed human design teams through a human subject study that includes an abrupt problem change. The results demonstrate that, for this study, the AI boosts the initial performance of low-performing teams before …},
author = {Guanglu Zhang and Ayush Raina and Jonathan Cagan and Christopher McComb},
citation = {Design Studies 72, 100990, 2021},
journal = {Design Studies},
pages = {100990},
pub_year = {2021},
publisher = {Elsevier},
title = {A cautionary tale about the impact of AI on human design teams},
volume = {72}
}
| {
"ENTRYTYPE": "article",
"ID": "zhang2021acautionarytale",
"abstract": "Recent advances in artificial intelligence (AI) offer opportunities for integrating AI into human design teams. Although various AIs have been developed to aid engineering design, the impact of AI usage on human design teams has received scant research attention. This research assesses the impact of a deep learning AI on distributed human design teams through a human subject study that includes an abrupt problem change. The results demonstrate that, for this study, the AI boosts the initial performance of low-performing teams before …",
"author": "Guanglu Zhang and Ayush Raina and Jonathan Cagan and Christopher McComb",
"citation": "Design Studies 72, 100990, 2021",
"conference": null,
"journal": "Design Studies",
"number": null,
"pages": "100990",
"pub_year": 2021,
"publisher": "Elsevier",
"title": "A cautionary tale about the impact of AI on human design teams",
"volume": "72"
} | 0P9w_S0AAAAJ:q3oQSFYPqjQC | 107 | /scholar?hl=en&cites=13862579487630350310 | [
"13862579487630350310"
] | https://www.sciencedirect.com/science/article/pii/S0142694X21000016 | /scholar?oi=bibs&hl=en&q=related:5tfSzpjGYcAJ:scholar.google.com/ | null | null | null | null | null | 7 | 29 | 32 | 39 | [
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@article{williams2019designrepositoryeffectiveness,
abstract = {Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as …},
author = {Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb},
citation = {Journal of Mechanical Design, 2019},
journal = {Journal of Mechanical Design},
pub_year = {2019},
title = {Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing}
}
| {
"ENTRYTYPE": "article",
"ID": "williams2019designrepositoryeffectiveness",
"abstract": "Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as …",
"author": "Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb",
"citation": "Journal of Mechanical Design, 2019",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2019,
"publisher": null,
"title": "Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing",
"volume": null
} | 0P9w_S0AAAAJ:BqipwSGYUEgC | 107 | /scholar?hl=en&cites=3123781173515911231 | [
"3123781173515911231"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/141/11/111701/955346 | /scholar?oi=bibs&hl=en&q=related:PwB4Q23mWSsJ:scholar.google.com/ | null | null | null | 1 | 11 | 19 | 26 | 27 | 21 | [
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@article{mccomb2017optimizingdesignteams,
abstract = {The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Journal of Mechanical Design, 2017},
journal = {Journal of Mechanical Design},
pub_year = {2017},
title = {Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2017optimizingdesignteams",
"abstract": "The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Journal of Mechanical Design, 2017",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2017,
"publisher": null,
"title": "Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test",
"volume": null
} | 0P9w_S0AAAAJ:4DMP91E08xMC | 90 | /scholar?hl=en&cites=10487496979515429608 | [
"10487496979515429608"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/139/4/041101/421818 | /scholar?oi=bibs&hl=en&q=related:6NL_DvASi5EJ:scholar.google.com/ | null | 4 | 10 | 19 | 15 | 14 | 12 | 10 | 4 | [
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@article{mccomb2015rollingwiththe,
abstract = {Designers must often create solutions to problems that exhibit dynamic characteristics. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. This paper presents a cognitive study that was conducted to explore the manner in which design teams respond to such situations. In the study, teams of undergraduate engineering students sought to solve a design task that was subject to two large, unexpected changes in problem formulation that were introduced during solving. High-and low-performing teams demonstrated very different …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Design Studies 36, 99-121, 2015},
journal = {Design Studies},
pages = {99-121},
pub_year = {2015},
publisher = {Elsevier},
title = {Rolling with the Punches: An Examination of Team Performance in a Design Task Subject to Drastic Changes},
volume = {36}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2015rollingwiththe",
"abstract": "Designers must often create solutions to problems that exhibit dynamic characteristics. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. This paper presents a cognitive study that was conducted to explore the manner in which design teams respond to such situations. In the study, teams of undergraduate engineering students sought to solve a design task that was subject to two large, unexpected changes in problem formulation that were introduced during solving. High-and low-performing teams demonstrated very different …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Design Studies 36, 99-121, 2015",
"conference": null,
"journal": "Design Studies",
"number": null,
"pages": "99-121",
"pub_year": 2015,
"publisher": "Elsevier",
"title": "Rolling with the Punches: An Examination of Team Performance in a Design Task Subject to Drastic Changes",
"volume": "36"
} | 0P9w_S0AAAAJ:WF5omc3nYNoC | 90 | /scholar?hl=en&cites=4707453472868866956 | [
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@article{mccomb2017capturinghumansequence-learning,
abstract = {Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Journal of Mechanical Design 139 (9), 2017},
journal = {Journal of Mechanical Design},
number = {9},
pub_year = {2017},
publisher = {ASME},
title = {Capturing Human Sequence-Learning Abilities in Configuration Design Tasks through Markov Chains},
volume = {139}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2017capturinghumansequence-learning",
"abstract": "Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Journal of Mechanical Design 139 (9), 2017",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": "9",
"pages": null,
"pub_year": 2017,
"publisher": "ASME",
"title": "Capturing Human Sequence-Learning Abilities in Configuration Design Tasks through Markov Chains",
"volume": "139"
} | 0P9w_S0AAAAJ:QIV2ME_5wuYC | 75 | /scholar?hl=en&cites=6286221499622255408 | [
"6286221499622255408"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/139/9/091101/383821 | /scholar?oi=bibs&hl=en&q=related:MGto6CUlPVcJ:scholar.google.com/ | null | 2 | 6 | 11 | 11 | 14 | 9 | 12 | 10 | [
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@article{mccomb2017miningprocessheuristics,
abstract = {Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Journal of Mechanical Design, 2017},
journal = {Journal of Mechanical Design},
pub_year = {2017},
publisher = {ASME},
title = {Mining Process Heuristics from Designer Action Data via Hidden Markov Models}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2017miningprocessheuristics",
"abstract": "Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Journal of Mechanical Design, 2017",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2017,
"publisher": "ASME",
"title": "Mining Process Heuristics from Designer Action Data via Hidden Markov Models",
"volume": null
} | 0P9w_S0AAAAJ:dhFuZR0502QC | 72 | /scholar?hl=en&cites=3594119212580805377 | [
"3594119212580805377"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/139/11/111412/375543 | /scholar?oi=bibs&hl=en&q=related:ARtJomHg4DEJ:scholar.google.com/ | null | 2 | 7 | 8 | 12 | 13 | 10 | 10 | 8 | [
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@article{alzayed2021areyoufeeling,
abstract = {Having empathy in the design process can help engineers relate to the end-user by identifying what and why certain experiences are meaningful. While there have been efforts to identify the factors that impact empathic tendencies in engineering, there has been limited evidence on how a students’ trait empathy or empathic self-efficacy develops over a design project or what factors impact this development. The current study was developed to explore the development of students’ trait empathy and empathic self-efficacy development and identify the underlying impact of the design project’s context and course instructor through a study with 103 engineering students. Students’ trait empathy and empathic self-efficacy were measured across each of the four design stages (problem formulation, concept generation, concept selection, and final conceptual design) during an 8-week project. The results highlight that …},
author = {Mohammad Alsager Alzayed and Christopher McComb and Jessica Menold and Jacquelyn Huff and Scarlett R Miller},
citation = {Journal of Mechanical Design, 2021},
journal = {Journal of Mechanical Design},
pub_year = {2021},
title = {Are you feeling me? An exploration of empathy development in engineering design education}
}
| {
"ENTRYTYPE": "article",
"ID": "alzayed2021areyoufeeling",
"abstract": "Having empathy in the design process can help engineers relate to the end-user by identifying what and why certain experiences are meaningful. While there have been efforts to identify the factors that impact empathic tendencies in engineering, there has been limited evidence on how a students’ trait empathy or empathic self-efficacy develops over a design project or what factors impact this development. The current study was developed to explore the development of students’ trait empathy and empathic self-efficacy development and identify the underlying impact of the design project’s context and course instructor through a study with 103 engineering students. Students’ trait empathy and empathic self-efficacy were measured across each of the four design stages (problem formulation, concept generation, concept selection, and final conceptual design) during an 8-week project. The results highlight that …",
"author": "Mohammad Alsager Alzayed and Christopher McComb and Jessica Menold and Jacquelyn Huff and Scarlett R Miller",
"citation": "Journal of Mechanical Design, 2021",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2021,
"publisher": null,
"title": "Are you feeling me? An exploration of empathy development in engineering design education",
"volume": null
} | 0P9w_S0AAAAJ:08ZZubdj9fEC | 56 | /scholar?hl=en&cites=3527662701135177656 | [
"3527662701135177656"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/143/11/112301/1087576 | /scholar?oi=bibs&hl=en&q=related:uKeOTInG9DAJ:scholar.google.com/ | null | null | null | null | 2 | 12 | 11 | 15 | 15 | [
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@article{lapp2019kaboom:anagent-based,
abstract = {The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton …},
author = {Samuel Lapp and Kathryn Jablokow and Christopher McComb},
citation = {Design Science, 2019},
journal = {Design Science},
pub_year = {2019},
title = {KABOOM: An Agent-Based Model for Simulating Cognitive Style in Team Problem Solving}
}
| {
"ENTRYTYPE": "article",
"ID": "lapp2019kaboom:anagent-based",
"abstract": "The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton …",
"author": "Samuel Lapp and Kathryn Jablokow and Christopher McComb",
"citation": "Design Science, 2019",
"conference": null,
"journal": "Design Science",
"number": null,
"pages": null,
"pub_year": 2019,
"publisher": null,
"title": "KABOOM: An Agent-Based Model for Simulating Cognitive Style in Team Problem Solving",
"volume": null
} | 0P9w_S0AAAAJ:ZHo1McVdvXMC | 54 | /scholar?hl=en&cites=4739586421082257884,30128802387191467 | [
"4739586421082257884",
"30128802387191467"
] | https://www.cambridge.org/core/journals/design-science/article/kaboom-an-agentbased-model-for-simulating-cognitive-style-in-team-problem-solving/DA2EA6A5A1E4472707B9C8C5FF642EA0 | /scholar?oi=bibs&hl=en&q=related:3HnfEr1kxkEJ:scholar.google.com/ | null | null | null | null | 5 | 11 | 14 | 14 | 10 | [
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] | null | null |
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