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1. Approaches for Supporting Exploration for Analogical Inspiration With Behavior, Material and Component Based Structural Representations of Patent Databases

   https://doi.org/10.1115/DETC2018-85591  

   Song, Hyeonik ; Fu, Katherine ( August 2018 , ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 2A: 44th Design Automation Conference)

   This paper presents an explorative-based computational methodology to aid the analogical retrieval process in design-by-analogy practice. The computational methodology, driven by Nonnegative Matrix Factorization (NMF), iteratively builds a hierarchical repositories of design solutions within which clusters of design analogies can be explored by designers. In the work, the methodology has been applied on a large repository of mechanical design related patents, processed to contain only component-, behavior-, or material-based content, to demonstrate that unique and valuable attribute-based analogical inspiration can be discovered from different representations of patent data. For explorative purposes, the hierarchical repositories have been visualized with a three-dimensional hierarchical structure and two-dimensional bar graph structure, which can be used interchangeably for retrieving analogies. This paper demonstrates that the explorative-based computational methodology provides designers an enhanced control over design repositories, empowering them to retrieve analogical inspiration for design-by-analogy practice. 

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2. LDA v. LSA: A Comparison of Two Computational Text Analysis Tools for the Functional Categorization of Patents

   Cvitanic, T. ; Lee, B. ; Song, H. I. ; Fu, K. ; Rosen, D. ( January 2016 , International Conference on Case-Based Reasoning)

   One means to support for design-by-analogy (DbA) in practice involves giving designers efficient access to source analogies as inspiration to solve problems. The patent database has been used for many DbA support efforts, as it is a preexisting repository of catalogued technology. Latent Semantic Analysis (LSA) has been shown to be an effective computational text processing method for extracting meaningful similarities between patents for useful functional exploration during DbA. However, this has only been shown to be useful at a small-scale (100 patents). Considering the vastness of the patent database and realistic exploration at a large scale, it is important to consider how these computational analyses change with orders of magnitude more data. We present analysis of 1,000 random mechanical patents, comparing the ability of LSA to Latent Dirichlet Allocation (LDA) to categorize patents into meaningful groups. Resulting implications for large(r) scale data mining of patents for DbA support are detailed. 

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3. Similarities and Differences in Human Vs. Computational Representations of Non-Semantic Inspirational Design Stimuli

   https://doi.org/10.1115/DETC2023-115108  

   Kwon, Elisa ; Goucher-Lambert, Kosa ( August 2023 , ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   As inspirational stimuli can assist designers with achieving enhanced design outcomes, supporting the retrieval of impactful sources of inspiration is important. Existing methods facilitating this retrieval have relied mostly on semantic relationships, e.g., analogical distances. Increasingly, data-driven methods can be leveraged to represent diverse stimuli in terms of multi-modal information, enabling designers to access stimuli in terms of less explored, non-text-based relationships. Toward improved retrieval of multi-modal representations of inspirational stimuli, this work compares human-evaluated and computationally derived similarities between stimuli in terms of non-text-based visual and functional features. A human subjects study (n = 36) was conducted where similarity assessments between triplets of 3D-model parts were collected and used to construct psychological embedding spaces. Distances between unique part embeddings were used to represent similarities in terms of visual and functional features. Obtained distances were compared with computed distances between embeddings of the same stimuli generated using artificial intelligence (AI)-based deep-learning approaches. When used to assess similarity in appearance and function, these representations were found to be largely consistent, with highest agreement found when assessing pairs of stimuli with low similarity. Alignment between models was otherwise lower when identifying the same pairs of stimuli with higher levels of similarity. Importantly, qualitative data also revealed insights regarding how humans made similarity assessments, including more abstract information not captured using AI-based approaches. Toward providing inspiration to designers that considers design problems, ideas, and solutions in terms of non-text-based relationships, further exploration of how these relationships are represented and evaluated is encouraged.

    

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4. Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach

   https://doi.org/10.1115/DETC2020-22613  

   Hans, Atharva ; Chaudhari, Ashish M. ; Bilionis, Ilias ; Panchal, Jitesh H. ( August 2020 , ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   null (Ed.)

   Extracting an individual’s knowledge structure is a challenging task as it requires formalization of many concepts and their interrelationships. While there has been significant research on how to represent knowledge to support computational design tasks, there is limited understanding of the knowledge structures of human designers. This understanding is necessary for comprehension of cognitive tasks such as decision making and reasoning, and for improving educational programs. In this paper, we focus on quantifying theory-based causal knowledge, which is a specific type of knowledge held by human designers. We develop a probabilistic graph-based model for representing individuals’ concept-specific causal knowledge for a given theory. We propose a methodology based on probabilistic directed acyclic graphs (DAGs) that uses logistic likelihood function for calculating the probability of a correct response. The approach involves a set of questions for gathering responses from 205 engineering students, and a hierarchical Bayesian approach for inferring individuals’ DAGs from the observed responses. We compare the proposed model to a baseline three-parameter logistic (3PL) model from the item response theory. The results suggest that the graph-based logistic model can estimate individual students’ knowledge graphs. Comparisons with the 3PL model indicate that knowledge assessment is more accurate when quantifying knowledge at the level of causal relations than quantifying it using a scalar ability parameter. The proposed model allows identification of parts of the curriculum that a student struggles with and parts they have already mastered which is essential for remediation.

    

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5. Quantifying Individuals' Theory-based Knowledge using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach

   Hans, Atharva ; Chaudhari, Ashish M. ; Bilionis, Ilias ; Panchal, Jitesh H. ( January 2020 , ASME 2020 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2020))

   Extracting an individual’s knowledge structure is a challenging task as it requires formalization of many concepts and their interrelationships. While there has been significant research on how to represent knowledge to support computational design tasks, there is limited understanding of the knowledge structures of human designers. This understanding is necessary for comprehension of cognitive tasks such as decision making and reasoning, and for improving educational programs. In this paper, we focus on quantifying theory-based causal knowledge, which is a specific type of knowledge held by human designers. We develop a probabilistic graph-based model for representing individuals’ concept-specific causal knowledge for a given theory. We propose a methodology based on probabilistic directed acyclic graphs (DAGs) that uses logistic likelihood function for calculating the probability of a correct response. The approach involves a set of questions for gathering responses from 205 engineering students, and a hierarchical Bayesian approach for inferring individuals’ DAGs from the observed responses. We compare the proposed model to a baseline three-parameter logistic (3PL) model from the item response theory. The results suggest that the graph-based logistic model can estimate individual students’ knowledge graphs. Comparisons with the 3PL model indicate that knowledge assessment is more accurate when quantifying knowledge at the level of causal relations than quantifying it using a scalar ability parameter. The proposed model allows identification of parts of the curriculum that a student struggles with and parts they have already mastered which is essential for remediation. 

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