Discovering Structure in Design Databases Through Functional and Surface Based Mapping

2011 ◽  
Author(s):  
Katherine Fu ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky ◽  
Kristin Wood
Keyword(s):  
Design Space ◽  
Semantic Analysis ◽  
Computational Design ◽  
Design Data ◽  
Cross Domain ◽  
Design By Analogy ◽  
Automated Discovery ◽  
Surface Similarity ◽  
Design Databases ◽  
Functional Content

This work presents a methodology for discovering structure in design repository databases, toward the ultimate goal of stimulating designers through design-by-analogy. Using a Bayesian model combined with Latent Semantic Analysis for discovering structural form in data, an exploration of inherent structural forms, based on the content and similarity of design data, is undertaken to gain useful insights into the nature of the design space. In this work, the approach is applied to uncover structure in the U.S. patent database. More specifically, the functional content and surface content of the patents are processed and mapped separately, yielding structures that have the potential to develop a better understanding of the functional and surface similarity of patents. These results may provide a basis for automated discovery of cross domain analogy, among other implications for creating a computational design stimulation tool.

Discovering Structure in Design Databases Through Functional and Surface Based Mapping

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Katherine Fu ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky ◽  
Kristin Wood
Keyword(s):  
Semantic Analysis ◽  
Computational Design ◽  
Functional Similarity ◽  
Transfer Of Knowledge ◽  
Structural Form ◽  
Design Data ◽  
Cross Domain ◽  
Starting Point ◽  
Surface Content ◽  
Content Similarity

This work presents a methodology for discovering structure in design repository databases, toward the ultimate goal of stimulating designers through design-by-analogy. Using a Bayesian model combined with latent semantic analysis (LSA) for discovering structural form in data, an exploration of inherent structural forms, based on the content and similarity of design data, is undertaken to gain useful insights into the nature of the design space. In this work, the approach is applied to uncover structure in the U.S. patent database. More specifically, the functional content and surface content of the patents are processed and mapped separately, yielding structures that have the potential to develop a better understanding of the functional and surface similarity of patents. Structures created with this methodology yield spaces of patents that are meaningfully arranged into labeled clusters, and labeled regions, based on their functional similarity or surface content similarity. Examples show that cross-domain associations and transfer of knowledge based on functional similarity can be extracted from the function based structures, and even from the surface content based structures as well. The comparison of different structural form types is shown to yield different insights into the arrangement of the space, the interrelationships between the patents, and the information within the patents that is attended to—enabling multiple representations of the same space to be easily accessible for design inspiration purposes. In addition, the placement of a design problem in the space effectively points to the most relevant cluster of patents in the space as an effective starting point of stimulation. These results provide a basis for automated discovery of cross-domain analogy, among other implications for creating a computational design stimulation tool.

Testing the Basis for an Automated Design-by-Analogy Tool Through Comparison to Expert Thinking

2013 ◽  
Author(s):  
Katherine Fu ◽  
Joel Chan ◽  
Christian Schunn ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Mental Models ◽  
Common Ground ◽  
Computational Design ◽  
Automated Design ◽  
Design Tool ◽  
Computational Method ◽  
Prior Work ◽  
Design By Analogy ◽  
Design Databases

Our prior work presented a method for computationally structuring patent databases as a basis for an automated design-by-analogy tool. In order to examine the validity and sensibility of the prior work as the basis for a design tool, its output is compared in detail to expert designers’ mental models of the domain being structured, i.e., a set of 45 patents and their inter-relationships. The comparison sought first to gauge the intuitiveness and sensibility of the computational method of structuring to human minds, and further to ascertain whether any differences between the method’s and the experts’ structures indicate potentially novel or surprising ways of approaching the space of patents, or indicate that the output was nonsensical, invalid or needing modification in order to be useable. The results indicate that, when compared to expert thinking, the computationally generated structure is sensible in its clustering of patents and in its organization of these clusters into a structure or space. The results also suggest that the computationally-generated structure represents a version of the patent space upon which experts can find common ground and consensus — making it likely to be intuitive and accessible to a broad cohort of designers. Thus, the prior work which presented a computational method for structuring design databases has been found to offer a resource-efficient way of usefully representing the space that is sensible to expert designers, while still preserving an element of surprise and unexpectedness, making it promising as the basis for a computational design-by-analogy inspiration tool.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Enriching Topic Coherence on Reviews for Cross-Domain Recommendation

2020 ◽  
Author(s):  
Mala Saraswat ◽  
Shampa Chakraverty
Keyword(s):  
Topic Modeling ◽  
Semantic Analysis ◽  
User Generated Content ◽  
Core Concept ◽  
Semantic Clustering ◽  
Cross Domain ◽  
Semantic Coherence ◽  
Reference Corpus ◽  
Multiple Domains ◽  
Explicit Semantic Analysis

Abstract With the advent of e-commerce sites and social media, users express their preferences and tastes freely through user-generated content such as reviews and comments. In order to promote cross-selling, e-commerce sites such as eBay and Amazon regularly use such inputs from multiple domains and suggest items with which users may be interested. In this paper, we propose a topic coherence-based cross-domain recommender model. The core concept is to use topic modeling to extract topics from user-generated content such as reviews and combine them with reliable semantic coherence techniques to link different domains, using Wikipedia as a reference corpus. We experiment with different topic coherence methods such as pointwise mutual information (PMI) and explicit semantic analysis (ESA). Experimental results presented demonstrate that our approach, using PMI as topic coherence, yields 22.6% and using ESA yields 54.4% higher precision as compared with cross-domain recommender system based on semantic clustering.

Negotiating human engagement and the fixity of computational design: Toward a performative design space for the differently-abled bodymind

2020 ◽  
Vol 18 (2) ◽  
pp. 174-193
Author(s):  
Sean Ahlquist
Keyword(s):  
Environmental Factors ◽  
Structural Optimization ◽  
Design Process ◽  
Design Space ◽  
Computational Design ◽  
Social Nature ◽  
And Performance ◽  
The Moment ◽  
Cultural Entity

Computational design affords agency: the ability to orchestrate the material, spatial, and technical architectural system. In this specific case, it occurs through enhanced, authored means to facilitate making and performance—typically driven by concerns of structural optimization, material use, and responsivity to environmental factors—of an atmospheric rather than social nature. At issue is the positioning of this particular manner of agency solely with the architect auteur. This abruptly halts—at the moment in which fabrication commences—the ability to amend, redefine, or newly introduce fundamentally transformational constituents and their interrelationships and, most importantly, to explore the possibility for extraordinary outcomes. When the architecture becomes a functional, social, and cultural entity, in the hands of the idealized abled-bodied user, agency—especially for one of an otherly body or mind—is long gone. Even an empathetic auteur may not be able to access the motivations of the differently-abled body and neuro-divergent mind, effectively locking the constraints of the design process, which creates an exclusionary system to those beyond the purview of said auteur. It can therefore be deduced that the mechanisms or authors of a conventional computational design process cannot eradicate the exclusionary reality of an architectural system. Agency is critical, yet a more expansive terminology for agent and agency is needed. The burden to conceive of capacities that will always be highly temporal, social, unpredictable, and purposefully unknown must be shifted far from the scope of the traditional directors of the architectural system. Agency, and who it is conferred upon, must function in a manner that dissolves the distinctions between the design, the action of designing, the author of design, and those subjected to it.

Computational Methods Applied to Modelica Simulation Models in a Web Based Framework

2005 ◽  
Author(s):  
Bjo¨rn Johansson
Keyword(s):  
Design Optimization ◽  
Computational Methods ◽  
Probabilistic Analysis ◽  
Service Oriented Architecture ◽  
Simulation Models ◽  
Computational Design ◽  
Design Data ◽  
Web Based ◽  
Actuation System ◽  
Service Oriented

In this paper, it is illustrated how computational design methods such as design optimization and probabilistic analysis is applied to system simulation models in a web based framework. Special emphasis is given models defined in the Modelica modeling language. An XML-based information system for representation and management of design data for use together with Modelica models as well as other types of models is proposed. This approach introduces a separation between the model of the system and data related to the design of the product. This is important in order to facilitate the use of computational methods in a generic way. A web based framework for integration of simulation models and computational methods is further illustrated. The framework is based on open standards for distributed computing and enables so-called service oriented architecture. Finally, an example is presented, where design optimization and probabilistic analysis is carried out on a Modelica model of an aircraft actuation system using the proposed and implemented tools and methods.

scholarly journals Quantitative Comparison of High- and Low-Performing Teams in a Design Task Subject to Drastic Changes

2014 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Design Space ◽  
New Technology ◽  
Problem Formulation ◽  
Target Area ◽  
Design Data ◽  
Design Task ◽  
High Performing ◽  
Engineering Teams ◽  
Solution Strategies ◽  
The Face

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 exploration around a target area of the design space. These strategic differences are interpreted with respect to cognitive load theory and goal theory. The results raise questions as to the relationship between characteristics of design problems and solution strategies. In addition, an attempt at increasing the teams’ resilience in the face of unexpected changes is introduced by encouraging early divergent search.

Data-Driven Design-by-Analogy: State of the Art

2021 ◽  
Author(s):  
Shuo Jiang ◽  
Jie Hu ◽  
Jianxi Luo
Keyword(s):  
Design Methodology ◽  
Data Science ◽  
State Of The Art ◽  
Data Driven ◽  
Literature Analysis ◽  
Target Domain ◽  
Individual Study ◽  
Source Domain ◽  
Design By Analogy ◽  
Design Databases

Abstract Design-by-Analogy (DbA) is a design methodology that draws inspiration from a source domain to a target domain to generate new solutions to problems or designs, which can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. Herein, we survey the prior data-driven DbA studies and categorize and analyze individual study according to the data, methods and applications in four categories including analogy encoding, retrieval, mapping, and evaluation. Based on such structured literature analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field.

Whither design space?

2006 ◽  
Vol 20 (2) ◽  
pp. 63-82 ◽  
Author(s):  
ROBERT F. WOODBURY ◽  
ANDREW L. BURROW
Keyword(s):  
Network Structure ◽  
Design Space Exploration ◽  
Design Research ◽  
Design Space ◽  
Space Exploration ◽  
Computational Design ◽  
Exploration Process ◽  
Computational Structures

Design space exploration is a long-standing focus in computational design research. Its three main threads are accounts of designer action, development of strategies for amplification of designer action in exploration, and discovery of computational structures to support exploration. Chief among such structures is the design space, which is the network structure of related designs that are visited in an exploration process. There is relatively little research on design spaces to date. This paper sketches a partial account of the structure of both design spaces and research to develop them. It focuses largely on the implications of designers acting as explorers.

A Semantic Analysis of the Design Space of Input Devices

1990 ◽  
Vol 5 (2-3) ◽  
pp. 145-190 ◽  
Author(s):  
Jock Mackinlay ◽  
Stuart K. Card ◽  
George G. Robertson
Keyword(s):  
Design Space ◽  
Semantic Analysis ◽  
Input Devices
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