{"title":"Aligning Human and Computational Evaluations of Functional Design Similarity","authors":"Ananya Nandy, K. Goucher-Lambert","doi":"10.1115/detc2021-71905","DOIUrl":"https://doi.org/10.1115/detc2021-71905","url":null,"abstract":"\u0000 Function drives many early design considerations in product development. Therefore, finding functionally similar examples is important when searching for sources of inspiration or evaluating designs against existing technology. However, it is difficult to capture what people consider to be functionally similar and therefore, if measures that compare function directly from the products themselves are meaningful. In this work, we compare human evaluations of similarity to computationally determined values, shedding light on how quantitative measures align with human perceptions of functional similarity. Human perception of functional similarity is considered at two levels of abstraction: (1) the high-level purpose of a product, and (2) a detailed view of how the product works. Human evaluations of similarity are quantified by crowdsourcing 1360 triplet ratings at each functional abstraction, and then compared to similarity that is computed between functional models. We demonstrate how different levels of abstraction and the fuzzy line between what is considered “similar” and “similar enough” may impact how these similarity measures are utilized, finding that different measures better align with human evaluations along each dimension. The results inform how product similarity can be leveraged by designers. Therefore, applications lie in creativity support tools, such as those used for design-by-analogy, or future computational methods in design that incorporate product function in addition to form.","PeriodicalId":261968,"journal":{"name":"Volume 6: 33rd International Conference on Design Theory and Methodology (DTM)","volume":"25 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114122170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander R. Murphy, B. C. Watson, Megan E. Tomko, Ethan C. Hilton, J. Linsey
{"title":"A Review of Design-Related Literature Concerning Cognitive Processes, Prototyping Strategies, and Modeling Processes","authors":"Alexander R. Murphy, B. C. Watson, Megan E. Tomko, Ethan C. Hilton, J. Linsey","doi":"10.1115/detc2021-66994","DOIUrl":"https://doi.org/10.1115/detc2021-66994","url":null,"abstract":"\u0000 In industry and academia, designers and engineers use prototyping at various stages in the design process to evaluate progress, archive process, assess viability, and communicate mental models to a team. Cognitive processes not only play a huge role during the design process, but also have causal relationships with various prototyping strategies. However, these causal relationships are not well understood in the design field. This paper presents a review of design-field literature related to cognitive processes, prototyping strategies, and modeling processes to identify literature consensus, consolidate experimental results, and expose gaps in the literature. After analysis of the literature, Fixation, Team Cognition, Iterative Prototyping, and Rapid Prototyping are the most well researched areas, especially when compared to their effects on cognitive processes. Sunk Cost, Requirement Relaxation, and Expertise are areas that could be better understood. The relationships between relevant cognitive processes, prototyping strategies, and modeling processes are consolidated into a data visualization that invites researchers and novices alike to explore the field of design in a fresh way that could spark new research endeavors or provoke interest in the field. This literature review and analysis reveals trends in design research through a novel approach with an emphasis on cognition, as well as provides a consolidated cannon of work that gives a sense of what has already been done on these topics and what is still left to explore.","PeriodicalId":261968,"journal":{"name":"Volume 6: 33rd International Conference on Design Theory and Methodology (DTM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Framework to Study Human-AI Collaborative Design Space Exploration","authors":"Antoni Virós-i-Martin, Daniel Selva","doi":"10.1115/detc2021-67619","DOIUrl":"https://doi.org/10.1115/detc2021-67619","url":null,"abstract":"\u0000 This paper presents a framework to describe and explain human-machine collaborative design focusing on Design Space Exploration (DSE), which is a popular method used in the early design of complex systems with roots in the well-known design as exploration paradigm. The human designer and a cognitive design assistant are both modeled as intelligent agents, with an internal state (e.g., motivation, cognitive workload), a knowledge state (separated in domain, design process, and problem specific knowledge), an estimated state of the world (i.e., status of the design task) and of the other agent, a hierarchy of goals (short-term and long-term, design and learning goals) and a set of long-term attributes (e.g., Kirton’s Adaption-Innovation inventory style, risk aversion). The framework emphasizes the relation between design goals and learning goals in DSE, as previously highlighted in the literature (e.g., Concept-Knowledge theory, LinD model) and builds upon the theory of common ground from human-computer interaction (e.g., shared goals, plans, attention) as a building block to develop successful assistants and interactions. Recent studies in human-AI collaborative DSE are reviewed from the lens of the proposed framework, and some new research questions are identified. This framework can help advance the theory of human-AI collaborative design by helping design researchers build promising hypotheses, and design studies to test these hypotheses that consider most relevant factors.","PeriodicalId":261968,"journal":{"name":"Volume 6: 33rd International Conference on Design Theory and Methodology (DTM)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124700058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}