ccm/publications huggingface dataset:
Analyzing the Characteristics of Cognitive-Assistant-Facilitated Ideation Groups
Authors: Torsten Maier and Nicolas F Soria Zurita and Elizabeth Starkey and Daniel Spillane and Jessica Menold and Christopher McComb
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

The rapid digitalization of the world has affected engineering and design in a variety of ways, including the introduction of new computer-aided ideation tools. Cognitive assistants (CA), an increasingly common digital technology, use natural-language processing and artificial intelligence to provide computational support. Because cognitive assistants are capable of emulating humans in some tasks, they may be suited to support brainstorming activities when trained coaches or facilitators are not available. This study compared co-located brainstorming groups facilitated by human facilitators and a CA facilitator. Interaction Dynamics Notation was used to code the sessions, and Hidden Markov Models were used to define the group’s states. We found that human facilitation was associated with blocks/interruptions and responding to those while cognitive assistant facilitation was associated with deviations and …
Quantitative Comparison of High- and Low-Performing Teams in a Design Task Subject to Drastic Changes
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: ASME 2014 International Design Engineering Technical Conferences and …, 2014

Many design tasks are subject to changes in goals or constraints. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. A design team often cannot anticipate such changes, yet they pose a considerable challenge. This paper presents a study where engineering teams sought to solve a design task that was subject to two large, unexpected changes in problem formulation that occurred during problem solving. Continuous design data was collected to observe how the designers responded to the changes. We show that high- and low-performing teams demonstrated very different approaches to solving the problem and overcoming the changes. In particular, high-performing teams achieved simple designs and extensively explored small portions of the design space; low-performing teams explored complex designs with little …
Reinforcement Learning for Efficient Design Space Exploration with Variable Fidelity Analysis Models
Authors: Akash Agrawal and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2023

Reinforcement learning algorithms can autonomously learn to search a design space for high-performance solutions. However, modern engineering often entails the use of computationally intensive simulation, which can lead to slower design timelines with highly iterative approaches such as reinforcement learning. This work provides a reinforcement learning framework that leverages models of varying fidelity to enable an effective solution search while reducing overall computational needs. Specifically, it utilizes models of varying fidelity while training the agent, iteratively progressing from low- to high fidelity. To demonstrate the effectiveness of the proposed framework, we apply it to two multimodal multi-objective constrained mixed integer nonlinear design problems involving the components of a ground and aerial vehicle. Specifically, for each problem, we utilize a high-fidelity and a low-fidelity deep neural …
Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search
Authors: Ayush Raina and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design, 2023

Building an Artificial Intelligence (AI) agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases data-driven learning toward existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network leverages self-generated experience to guide the search further. This framework first demonstrates an ability to discover high-performing …
Analysing the Effect of Self-Efficacy and Influencers on Design Team Performance
Authors: Harshika Singh and Gaetano Cascini and C McComb
Published in: Proceedings of the Design Society: DESIGN Conference 1, 2571-2580, 2020

Social media influencers (SMI) are gaining interest and many are studying their influence on the online audience, little is known about the role played by them in offline teams. One such attempt to study the effect of influencers in co-design team is presented in this paper, where individuals who are confident in their abilities drive the team process. Thus, self-efficacy is considered for determining influencer behaviour. Results expose the relationship between self-efficacy and influencer status on the design process, besides briefly highlighting the effects on above-average teams.
Enhancement of Shear Transfer in Composite Deck with Mechanical Fasteners
Authors: Christopher McComb and Fariborz M Tehrani
Published in: Engineering Structures 88, 251-261, 2015

Reinforced concrete construction and steel construction are two of the most common contemporary construction methods. Both methods make use of composite decks as floor systems and diaphragms. During the design of composite decks, the interaction between the steel deck and concrete slab is typically disregarded when calculating the strength of the composite diaphragm due to lack of complete shear transfer, as existing practices such as stamping are not effective to provide substantial composite action. This conservative assumption essentially results in non-efficient use of material. This paper presents a method for ensuring shear transfer through the use of conventional sheet metal screws, driven through the corrugated steel deck and embedded in the fresh concrete. This method is proposed for use in the field between formed steel beams. Both experimental and analytical studies support an increase in …
Combining Multi-body Dynamics and Potential Flow Simulation Methods to Model a Wave Energy Converter
Authors: Chris McComb and Michael Lawson and Yi-Hsiang Yu
Published in: First Marine Energy Technology Symposium, 2013

Numerical simulations that predict the dynamics and performance of wave energy converters (WECs) require the simulation of complex fluid-structure interactions between a WEC device and the wave environment. Navier-Stokes computational fluid dynamics (CFD) simulations and fully time-domain boundary integral equation methods (BIEMs) can be coupled with multi-body dynamics solvers to simulate these problems. However, the computational resources that are required to perform these types of high-fidelity simulations are significant, precluding the use of CFD and time-domain BIEM for design optimization. One method for reducing the numerical complexity of WEC simulations is to model the hydrodynamics using frequency-domain simulations, while model the dynamic motion of the WEC device in the time-domain using multi-body dynamics methods. The primary objective of the work presented herein is to develop such a numerical tool using the multi-body solver SimMechanics and the frequency-domain hydrodynamics code WAMIT. The numerical tool was developed so that WEC devices comprised of arbitrarily connected bodies, power-take-off (PTO) systems, and mooring systems can be simulated in operational sea states, where the wave environment can be modeled under linear assumptions. The remainder of this paper describes the development and verification of the numerical tool.
Communication in AI-assisted Teams During an Interdisciplinary Drone Design Problem
Authors: Joshua T Gyory and Binyang Song and Jonathan Cagan and Christopher McComb
Published in: International Conference on Engineering Design, 2021

Human-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more …
Data-Driven Heuristic Induction from Human Design Behavior
Authors: Lucas Puentes and Jonathan Cagan and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2020

Through experience, designers develop guiding principles, or heuristics, to aid decision-making in familiar design domains. Generalized versions of common design heuristics have been identified across multiple domains and applied by novices to design problems. Previous work leveraged a sample of these common heuristics to assist in an agent-based design process, which typically lacks heuristics. These predefined heuristics were translated into sequences of specifically applied design changes that followed the theme of the heuristic. To overcome the upfront burden, need for human interpretation, and lack of generality of this manual process, this paper presents a methodology that induces frequent heuristic sequences from an existing timeseries design change dataset. Individual induced sequences are then algorithmically grouped based on similarity to form groups that each represent a shared …
Stochastic Stackelberg Games for Agent-Driven Robust Design
Authors: Sean C Rismiller and Jonathan Cagan and Christopher McComb
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

Products must often endure unpredictable and challenging conditions while fulfilling their intended functions. Game-theoretic methods make it possible for designers to design solutions that are robust against complicated conditions, however, these methods are often specific to the problems they investigate. This work introduces the Game-Augmented Robust Simulated Annealing Teams (GARSAT) framework, a game-theoretic agent-based architecture that generates solutions robust to variation, and models problems with elementary information, making it easily extendable. The platform was used to generate designs under consideration of a multidimensional attack. Designs were produced under various adversarial settings and compared to designs generated without considering adversaries to validate the model. The process successfully created robust designs able to withstand multiple combined …
A Review of Part Filtering Methods for Additive Manufacturing
Authors: Jennifer E. Bracken and Christopher McComb and Timothy W. Simpson and Kathryn Jablokow
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

As additive manufacturing (AM) increases in popularity, many companies seek to identify which parts can be produced via AM. This has led to new areas of research known as “part filtering”, “part selection”, or “part identification” for AM. Numerous methods have been proposed to quantify the suitability of a design to be made with AM, and each has its own benefits and drawbacks. This paper reviews popular methods of part filtering and elaborates on the advantages and disadvantages of the various approaches. The approaches for part filtering, and the example methods, are categorized and sorted along a continuum of opportunistic and restrictive methods in order to clarify use cases for various part filtering techniques. The approaches are also examined through the lens of specificity of process, as some are designed to be process agnostic, while others are customized for a specific AM technology or even a …
Towards an Ontology of Cognitive Assistants
Authors: Torsten Maier and Jessica Menold and Christopher McComb
Published in: International Conference on Engineering Design, 2019

Cognitive assistants such as IBM Watson and Siri are at the forefront of social and technological innovation and have the potential to solve many unique problems. However, the lack of standardization and classification within the field impedes critical analysis of existing cognitive assistants and may further inhibit their growth into more useful applications. This paper discusses the development of an ontology, its classes, and subclasses that may serve as a foundation for defining and differentiating CAs. Specifically, the four suggested classes include: learning, intelligence, autonomy, and communication. Various assistants are described and categorized using the proposed system. Our novel ontological framework is the first step towards a classification system for this burgeoning field.
Empathic creativity: can trait empathy predict creative concept generation and selection?
Authors: Mohammad Alsager Alzayed and Scarlett R Miller and Christopher McComb
Published in: AI EDAM 35 (4), 369-383, 2021

Over the past decade, engineering design research has seen a significant surge of the discussion of empathy. As such, design researchers have been devoted in devising and assessing empathic design activities. While prior research has examined the utility of empathic design experiences on driving creative concept generation, little is known about the role of a designer's empathic tendencies in driving creative concept generation and selection in an engineering design project. Without this knowledge, we cannot be sure if, when, or how empathy influences the design process. Thus, the main goal of this paper was to identify the role of trait empathy in creative concept generation and selection in an engineering design student project. In order to achieve this objective, a study was conducted with 103 first-year engineering students during two design stages of an 8-week design project (concept generation and …
Data on the Configuration Design of Internet-Connected Home Cooling Systems by Engineering Students
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Data in Brief, 2017

This experiment was carried out to record the step-by-step actions that humans take in solving a configuration design problem, either in small teams or individually. Specifically, study participants were tasked with configuring an internet-connected system of products to maintain temperature within a home, subject to cost constraints. Every participant was given access to a computer-based design interface that allowed them to construct and assess solutions. The interface was also used to record the data that is presented here. In total, data was collected for 68 participants, and each participant was allowed to perform 50 design actions in solving the configuration design problem. Major results based on the data presented here have been reported separately, including initial behavioral analysis (McComb et al.) [1], [2] and design pattern assessments via Markovian modeling (McComb et al., 2017; McComb et al., 2017 …
Linking Properties of Design Problem to Optimal Team Characteristics
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: ASME 2016 International Design Engineering Technical Conferences and …, 2016

A team with the right characteristics can exceed the sum of their individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is crucial that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of a design problem can be used to select the best values for team characteristics. Two characteristics are considered: team size and interaction frequency. A computational model of design teams that has been shown to effectively emulate human team behavior is leveraged to pinpoint optimized team characteristics for solving a variety of fluid and structural design problems. The nature of each design problem is characterized with respect to local and global behavior of the design space, alignment between objective functions, and the resources allotted for solving the problem …
Fairness and Manipulation: An Empirical Study of Arrow’s Impossibility Theorem
Authors: Christopher McComb and Kosa Goucher-Lambert and Jonathan Cagan
Published in: International Conference on Engineering Design, 2015

The design process often requires work by teams, rather than individuals. During these times it is likely that situations will arise in which members of a team have different opinions, yet a group decision must still be made. Unfortunately, Arrow’s Impossibility Theorem indicates that there is no method for aggregating group preferences that will always satisfy a small number of “fair” conditions. This work seeks to identify methods of combining individual preferences that can come close to satisfying Arrow’s conditions. Experiential conjoint analysis was used to obtain empirical utility functions for drinking mug designs. A number of functions for constructing group preference were then analysed using both empirical conjoint preferences and randomly generated preferences. This analysis involved checking each of Arrow’s conditions, as well as computing the likelihood that a method will be susceptible to manipulation by a dishonest individual. Based on the results, methods that should be used to aggregate group preference in practice are recommended.
Arctic Coastal Hazard Assessment Considering Permafrost Thaw Subsidence, Coastal Erosion, and Flooding
Authors: Ziyi Wang and Ming Xiao and Dmitry Nicolsky and Vladimir Romanovsky and Christopher McComb and Louise Farquharson
Published in: Environmental Research Letters, 2023

The thawing of permafrost in the Arctic has led to an increase in coastal land loss, flooding, and ground subsidence, seriously threatening civil infrastructure and coastal communities. However, a lack of tools for synthetic hazard assessment of the Arctic coast has hindered effective response measures. We developed a holistic framework, the Arctic Coastal Hazard Index (ACHI), to assess the vulnerability of Arctic coasts to permafrost thawing, coastal erosion, and coastal flooding. We quantified the coastal permafrost thaw potential (PTP) through regional assessment of thaw subsidence using ground settlement index. The calculations of the ground settlement index involve utilizing projections of permafrost conditions, including future regional mean annual ground temperature, active layer thickness, and talik thickness. The predicted thaw subsidence was validated through a comparison with observed long-term …
Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling
Authors: Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2022

The intersection between engineering design, manufacturing, and artificial intelligence offers countless opportunities for breakthrough improvements in how we develop new technology. However, achieving this synergy between the physical and the computational worlds involves overcoming a core challenge: few specialists educated today are trained in both engineering design and artificial intelligence. This fact, combined with the recency of both fields’ adoption and the antiquated state of many institutional data management systems, results in an industrial landscape that is relatively devoid of high-quality data and individuals who can rapidly use that data for machine learning and artificial intelligence development. In order to advance the fields of engineering design and manufacturing to the next level of preparedness for the development of effective artificially intelligent, data-driven analytical and …
Comparing Strategies for Visualizing the High-Dimensional Exploration Behavior of CPS Design Agents
Authors: Akash Agrawal and Christopher McComb
Published in: 2022 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2022

The design of cyber-physical systems often involves search within high-dimensional design spaces. When evaluating the performance of algorithms in tasks such as these, the patterns of exploration are often informative and can help support algorithm selection. However, accurately representing these patterns in a way that is human understandable while still preserving the nuanced search complexities in the high-dimensional space is nontrivial. This work specifically examines approaches for visualizing the search trajectories of reinforcement learning agents. We assess trajectories on two exemplar problems: the design of a racecar and the design of an aerial vehicle. We compare and contrast the visualizations produced using PCA, t-SNE, UMAP, TriMap, and PaCMAP. Future work should extend this comparison to a wider variety of exemplar design problems and consider the additional challenges posed by set …
No Time for That?: An Investigation of Mindfulness and Stress in First-Year Engineering Design
Authors: Hannah Nolte and Jacquelyn Huff and Christopher McComb
Published in: Design Science, 2022

Engineering design induces mental stress for students and the sources of stress for each stage of design are unique. Therefore, strategies are needed to manage the stress of engineering design that are applicable across the design process. This study investigated the effect of a brief mindfulness-based intervention on first-year students’ cognitive stress during concept generation, concept selection and physical modelling. It was found that the mindfulness-based intervention did increase one aspect of students’ state mindfulness (though the effect was small). While prior work indicates that increased mindfulness can lower perceived stress, the increase in students’ state mindfulness during this study was not found to have an observable impact on students’ stress experience. However, students were receptive to completing a mindfulness-based activity in-class and perceived multiple benefits. Physical modelling was …
Comparing Human and Cognitive Assistant Facilitated Brainstorming Sessions
Authors: Torsten Maier and Nicolas F. Soria Zurita and Elizabeth Starkey and Daniel Spillane and Jessica Menold and Christopher McComb
Published in: Journal of Engineering Design, 2022

The rapid digitalization of the world has affected engineering and design in a variety of ways, including the introduction of new computer-aided ideation tools. Cognitive assistants (CA), an increasingly common digital technology, use natural-language processing and artificial intelligence to provide computational support. Because CAs are capable of emulating humans in some tasks, they may be suited to support ideation activities when trained coaches or facilitators are not available. However, the effects and perception of CA-facilitated ideation are not fully understood. This study compared co-located brainstorming groups facilitated by human facilitators and a CA facilitator. We found that human facilitation was associated with the blocks/interruptions state because they used these moments to initiate facilitation; conversely, cognitive assistant facilitation was associated with the deviations and silence state. Human …