We describe three approaches to identify novel product affordances: affordance of absence; insights from lead users, specifically do-it-yourselfers (DIYers); and natural-language searches. While these approaches were separately pursued, we show their connection to each other in this paper. We begin by describing the affordance of absence, inspired by insights on affordances arising from a lack of resources. For example, in the absence of specialized tools, more general tools are used to accomplish similar tasks. Such absence clarifies how other tools could be modified to add relevant features and identifies critical features of the absent tool. In addition, the temporary removal of physical features and objects enables user interaction in ways that may not emerge in their presence. Affordance of absence has the potential to more fully specify affordances for a given object and to help overcome functional fixedness. For the second approach, we describe insights from DIYers obtained from the “IKEA hackers” online community. We consider DIYers lead users for seeking out and exploiting product affordances, often transforming product functions dramatically. We also discuss their projects through the lens of affordance of absence. For the third approach, we outline our natural-language approach to affordance extraction, beginning with consumer product reviews provided for Canadian Tire, a major Canadian retailer. We describe efforts toward automatically identifying less common affordances, and the use of cue phrases to highlight insightful DIY transformations from the IKEA hackers community. Finally, we comment on the potential value of this work for product design in general.

This paper presents a framework for the interactions between the processes of mapping and rerepresentation within analogy making. Analogical reasoning systems for use in design tasks require representations that are open to being reinterpreted. The framework, interpretation-driven mapping, casts the process of constructing an analogical relationship as requiring iterative, parallel interactions between mapping and interpreting. This paper argues that this interpretation-driven approach focuses research on a fundamental problem in analogy making: how do the representations that make new mappings possible emerge during the mapping process? The framework is useful for both describing existing analogy-making models and designing future ones. The paper presents a computational model informed by the framework Idiom, which learns ways to reinterpret the representations of objects as it maps between them. The results of an implementation in the domain of visual analogy are presented to demonstrate its feasibility. Analogies constructed by the system are presented as examples. The interpretation-driven mapping framework is then used to compare representational change in Idiom to that in three previously published systems.

The use of analytics has been emerging as a way to better understand the complex dynamics and resulting trends that occur when social and technical systems intersect. For instance, web analytics studies the intersection between society and the Internet to better understand use patterns and preferences. Business analytics studies the interfaces between human capital and technical systems in the context of corporate management and industrial production. Engineering design is ripe with such sociotechnical systems where consumers and engineered systems intersect producing a complex sociotechnical system marked by difficult to predict behavior and trends. In this paper, the paradigm of design analytics is further developed and used to study a diverse sociotechnical product. The product is a refrigerator that is hypothetically equipped with sensors and feedback mechanisms, and a simulator models the interactions of a population of 1000 users. Analyzing this sociotechnical product using design analytics demonstrates the ability to extract valuable insights at the juncture of people and technology. Insights into the leading behaviors of different populations are presented, and it is shown that design analytics and the associated tools can provide a platform for designers to develop better performing products that meet both explicit and implicit customer needs.

This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

When developing an artifact, designers must first understand the problem. This includes the benefits that the artifact must deliver and the user variation that is present. Each user has a unique set of human factors, preferences, personal knowledge, and solution constraints that could potentially influence the characteristics of the artifact. Currently, there is little work supporting the process of how to formally generate user-specific design specifications, resulting in ad hoc or a priori decisions when generating design specifications. Further, because most design processes generate design specifications manually, the number of design specifications is not typically addressed at the user level. This research presents an affordance-based approach for use in the early stages of design to help designers establish user-specific design specifications. This information can then be used in the creation of a system or set of systems that meets the demands of both the user(s) and the organization that is developing the artifact. An affordance-based approach is leveraged because it maintains the relational field of view among the user, existing artifacts, and the artifact(s) being designed. Once individual design specifications are generated, designers can use this information in later stages of the design process.

Performance assessment at early stages of buildings design is complicated by an inherent lack of information on the design and the uncertainty in how a building design may evolve to a final design. This pilot study reports on an initial quantification of such uncertainty associated with building energy performance and develops a method for informing decision makers of the risks in early design decisions under this uncertainty. Two case studies of building design decision situations under this uncertainty are explored along with using two different energy modeling tools: a reduced-order model and a high-order model. The intended contribution is to identify if a decision can be made with confidence in early design given a high level of uncertainty in the evolution of a design and what models can support decisions of this sort. Integration of the proposed decision support approach with a computer-aided design model is shown as well.

One mode of creative design is for designers to draw analogies that connect the design domain (e.g., a mechanical device) to some other domain from which inspiration is drawn (e.g., a biological system). The identification and application of analogies can be supported by software tools that store, structure, present, or propose source domain stimuli from which such analogies might be constructed. For these tools to be effective and not impact the design process in negative ways, they must fit well with the information and interaction needs of their users. However, the user requirements for these tools are seldom explicitly discussed. Furthermore, the literature that supports the identification of such requirements is distributed across a number of different domains, including those that address analogical design (especially biomimetics), creativity support tools, and human–computer interaction. The requirements that these literatures propose can be divided into those that relate to the information content that the tools provide (e.g., level of abstraction or mode of representation) and those that relate to the interaction qualities that the tools support (e.g., accessibility or shareability). Examining the relationships between these requirements suggests that tool developers should focus on satisfying the key requirements of open-endedness and accessibility while managing the conflicts between the other requirements. Attention to these requirements and the relationships between them promises to yield analogical design support tools that better permit designers to identify and apply source information in their creative work.

When considering a system that performs a role, it is often stated that performing that role is a function of the system. The general form of such statements is that “the function of S is R,” where S is the functioning system and R is the functional role it plays. However, such statements do not represent how that single function was selected from many possible alternatives. This article renders those alternatives explicit by revealing the other possible function statements that might be made when either S or R is being considered. In particular, two forms of selection are emphasized. First, when we say “the function of S is R,” there are typically many systems other than S that are required to be in operation for that role to be fulfilled. The functioning system, S, does not perform the role, R, all by itself, and those systems that support S in performing that role might also have been considered as functioning. Second, when we say, “the function of S is R,” there are typically many other roles that S plays apart from R, and those other roles might also have been considered functional. When we make function assignments, we select both the functioning system, S, and the functional role, R, from a range of alternatives. To emphasize these alternatives, this article develops a diagrammatic representation of multiple systems playing multiple roles in multiple supersystems.

Digital libraries of case studies of analogical design have been popular since their advent in the early 1990s. We consider four benefits of digital libraries of case studies of analogical design in the context of biologically inspired design. First, a digital library affords documentation. The 83 case studies in our work come from 8 years of extended, collaborative design projects in an interdisciplinary class on biologically inspired design. Second, a digital library provides on-demand access to the case studies. We describe a web-based library of case studies of biologically inspired design called the Design Study Library (DSL). Third, a compilation of case studies supports analyses of broader patterns and trends. As an example, an analysis of DSL's case studies found that environmental sustainability was a major factor in about a third of the case studies and an explicit design goal in about a fourth. Fourth, a digital library of case studies can support analogical learning. Preliminary results from an exploratory study indicate that DSL may support novice learning about the processes of biologically inspired design.

A platform for experimenting with population-based design exploration algorithms is presented, called Dexen. The platform has been developed in order to address the needs of two distinct groups of users loosely labeled as researchers and designers. Whereas the researchers group focuses on creating and testing customized toolkits, the designers group focuses on applying these toolkits in the design process. A platform is required that is scalable and extensible: scalable to allow computationally demanding population-based exploration algorithms to be executed on distributed hardware within reasonable time frames, and extensible to allow researchers to easily implement their own customized toolkits consisting of specialized algorithms and user interfaces. In order to address these requirements, a three-tier client–server system architecture has been used that separates data storage, domain logic, and presentation. This separation allows customized toolkits to be created for Dexen without requiring any changes to the data or logic tiers. In the logic tier, Dexen uses a programming model in which tasks only communicate through data objects stored in a key-value database. The paper ends with a case study experiment that uses a multicriteria evolutionary algorithm toolkit to explore alternative configurations for the massing and façade design of a large residential development. The parametric models for developing and evaluating design variants are described in detail. A population of design variants are evolved, a number of which are selected for further analysis. The case study demonstrates how evolutionary exploration methods can be applied to a complex design scenario without requiring any scripting.

Resilience is a key driver in the design of systems that must operate in an uncertain operating environment, and it is a key metric to assess the capacity for systems to perform within the specified performance envelop despite disturbances to their operating environment. This paper describes a graph spectral approach to calculate the resilience of complex engineered systems. The resilience of the design architecture of complex engineered systems is deduced from graph spectra. This is calculated from adjacency matrix representations of the physical connections between components in complex engineered systems. Furthermore, we propose a new method to identify the most vulnerable components in the design and design architectures that are robust to transmission of failures. Nonlinear dynamical system and epidemic spreading models are used to compare the failure propagation mean time transformation. Using these metrics, we present a case study based on the Advanced Diagnostics and Prognostics Testbed, which is an electrical power system developed at NASA Ames as a subsystem for the ramp system of an infantry fighting vehicle.

The objective of this research is to develop a computational representation of knowledge associated with affordance-based design (ABD). The ABD ontology formalizes the entities, properties, and relationships within the domains of ABD. The ontology enables designers to describe the affordances of existing products and specify the intended affordances of future products in line with ABD. The ontology consists of 14 concepts and 5 relationships. The ontology is developed using Protégé 4.3 and DL-query to query and reason with the ontology. The ontology is demonstrated using a consumer vacuum cleaner. The formal ontology serves as the basis for developing computer support for ABD applications. When implemented, these design tools will help designers manage the affordances of artifacts being designed, specifying the interacting entities of every affordance when a three-dimensional model of the artifact is available. Further, these software tools could be used to support ABD methods.

People interact with artifacts, either products or services, in their lives. These interactions are based on two-way communication between people and artifacts. The characteristics of artifacts that induce natural activities of people, affordances, play critical roles in making interactions successful and meaningful. The notion of affordance features, structural elements of artifacts that provide affordances, has been proposed earlier. In this paper, a methodological framework for design for affordances is proposed where repositories of affordance features are used. Affordances are identified through function–task interaction matrices or use activity observations. Using an affordance feature repository where many alternative structural elements for a specific affordance are stored together with corresponding design constraints and contexts, affordance features for those identified affordances are retrieved considering similarities between the target design constraints and those of the affordance features in the repositories. Using the clues given by such affordance features, a new affordance feature is to be designed through analogical reasoning. We present this design for affordance framework together with illustrative cases where various designers designed affordance features using affordance feature repositories.

Building performance simulation and genetic algorithms are powerful techniques for helping designers make better design decisions in architectural design optimization. However, they are very time consuming and require a significant amount of computing power. More time is needed when two techniques work together. This has become the primary impediment in applying design optimization to real-world projects. This study focuses on reducing the computing time in genetic algorithms when building simulation techniques are involved. In this study, we combine two techniques (offline simulation and divide and conquer) to effectively improve the run time in these architectural design optimization problems, utilizing architecture-specific domain knowledge. The improved methods are evaluated with a case study of a nursing unit design to minimize the nurses’ travel distance and maximize daylighting performance in patient rooms. Results show the computing time can be saved significantly during the simulation and optimization process.

How do humans judge the creativeness of an artwork or other artifact? This article suggests that such judgments are based on the pleasures of an aesthetic experience, which can be modeled as a mathematical product of psychological arousal and appraisal. The arousal stems from surprise, and is computed as a marginal entropy using information theory. The appraisal assigns meaning, by which the surprise is resolved, and is computed as a posterior probability using Bayesian theory. This model is tested by obtaining human ratings of surprise, meaning, and creativeness for artifacts in a domain of advertising design. The empirical results show that humans do judge creativeness as a product of surprise and meaning, consistent with the computational model of arousal and appraisal. Implications of the model are discussed with respect to advancing artificial intelligence in the arts as well as improving the computational evaluation of creativity in engineering and design.

Climate change, resource depletion, and worldwide urbanization feed the demand for more energy and resource-efficient buildings. Increasingly, architectural designers and consultants analyze building designs with easy-to-use simulation tools. To identify design alternatives with good performance, designers often turn to optimization methods. Randomized, metaheuristic methods such as genetic algorithms are popular in the architectural design field. However, are metaheuristics the best approach for architectural design problems that often are complex and ill defined? Metaheuristics may find solutions for well-defined problems, but they do not contribute to a better understanding of a complex design problem. This paper proposes surrogate-based optimization as a method that promotes understanding of the design problem. The surrogate method interpolates a mathematical model from data that relate design parameters to performance criteria. Designers can interact with this model to explore the approximate impact of changing design variables. We apply the radial basis function method, a specific type of surrogate model, to two architectural daylight optimization problems. These case studies, along with results from computational experiments, serve to discuss several advantages of surrogate models. First, surrogate models not only propose good solutions but also allow designers to address issues outside of the formulation of the optimization problem. Instead of accepting a solution presented by the optimization process, designers can improve their understanding of the design problem by interacting with the model. Second, a related advantage is that designers can quickly construct surrogate models from existing simulation results and other knowledge they might possess about the design problem. Designers can thus explore the impact of different evaluation criteria by constructing several models from the same set of data. They also can create models from approximate data and later refine them with more precise simulations. Third, surrogate-based methods typically find global optima orders of magnitude faster than genetic algorithms, especially when the evaluation of design variants requires time-intensive simulations.