Cross-domain Analogies Research Articles

Comparing Analogy-Based Methods-Bio-Inspiration and Engineering-Domain Inspiration for Domain Selection and Novelty.

This study aims to support designers in developing transformative solutions in the engineering discipline using the Design-by-Analogy ideation method. Design-by-Analogy involves drawing inspiration from the source domain and applying it to the target domain. Based on the conceptual distance between the two domains, analogies are classified as biological-(natural), cross-(distant-engineering), and within-(near-engineering) domain analogies. Real-world scenarios involve designers selecting analogies after seeking them across multiple domains. These selected analogies significantly influence the produced designs. However, the selection criteria of the analogy domain are unexplored in design research. We address this gap by investigating: (a) the influence of analogy domains on their selection frequency; and (b) the relationship between the frequency of selecting analogies from specific domains and the novelty of designs. The experiment involved twenty-six teams of novice product designers, who solved design problems aided by one analogical source from each domain. The results showed that biological analogies were frequently selected. While biological-domain analogies significantly increased the novelty of designs compared to the within-domain ones; no significant difference was found between the biological- and cross-domain analogies, suggesting that middle-domain analogies can be as effective as far-domain ones. The findings can support technological innovation by aiding the development of analogy search databases.

ERP evidence for temporal differences between cross-modal and cross-domain analogical reasoning

Previous studies have shown that individuals not only successfully engage in cross-domain analogies but also accomplish cross-modal reasoning. Yet, the behavioral representation and neurophysiological basis of cross-modal and cross-domain analogical reasoning remain unclear. This study established three analogical reasoning conditions by combining a multi-to-multi learning-test paradigm with a four‑term analogy paradigm: within-domain, cross-domain, and cross-modal conditions. Thirty participants were required to judge whether the relationship between C and D was the same as the learned relationship between A and B. Behavioral results revealed no significant differences in reaction times and accuracy between cross-domain and cross-modal conditions, but both conditions showed significantly lower accuracy than within-domain condition. ERP results indicated a larger P2 amplitude in the cross-modal condition, while a larger N400 amplitude was observed in the cross-domain condition. These findings suggest: (1) The P2 in cross-modal analogical reasoning is associated with more difficult access to cross-modal information. (2) The N400 in cross-domain analogical reasoning is related to more challenging semantic processing. This study provides the first evidence of behavioral and ERP differences between cross-modal and cross-domain analogical reasoning, deepening our understanding of the cognitive processes involved in cross-modal analogical reasoning.

SMFM-based analogy retrieval tool for the conceptual design of innovative products

Design-by-Analogy (DbA) is a powerful approach to innovative conceptual design. Cross-domain analogies are the stimulated sources of inspiration for generating new concepts. Therefore, how to efficiently retrieve them becomes an essential issue to be solved. This paper proposes a tool based on a structure-mapping function model (SMFM) for analogy retrieval. Inspired by the structure-mapping theory, SMFM considers two in-depth features: functional relation and causal relation between functions, which are expressed by function and meta-function concepts, respectively. SMFM serves to capture design knowledge from instances (i.e., engineering or biological systems) to establish a case database. The SMFM ontology is constructed for linking knowledge representation and analogy retrieval. Its meta-function, function, and flow terms are used to index the modeled instances so as to establish an index database. Analogy retrieval is realized by the ontology-based query expansion, vector space model and weighted cosine similarity. By inputting function or meta-function queries, we retrieve the required cross-domain analogies, whose design knowledge is displayed by function models to inspire target functions or meta-functions realization. Finally, the tool is evaluated for its effectiveness in cross-domain analogies retrieval. An application example illustrates its practicality in aiding innovative design. Additionally, an experiment was conducted to test the tool. The results indicate that our tool can assist users (e.g., engineering designers) to more quickly generate new schemes to realize target meta-functions and the quality of schemes is higher.

Facilitation method for the translation of biological systems to technical design solutions

Innovative designs can be limited by the experience and knowledge of the individual designers. These limitations can be combated by integrating knowledge from other fields of study. However the vernacular used by the respective fields creates a gap, which requires a process of facilitated translation to bridge. Specifically, biology is a field that has been used for inspiration for design solutions by engineers. This field, also known as biomimicry, has already proven to be a successful tool for identifying innovative inspiration for design but is under-utilized due to the difficulty of translating between biology and engineering. The proposed bioinspired design process discussed in this paper uses sketching of biological systems to create visual analogies that facilitate the translation from a biological system to technical design solution. Sketching of the inspiring biological system re-conceptualizes the information to aid in comprehension as well as establishes a clear path to cross-domain analogy identification to result in by innovative technical design solutions. This paper focuses on the theory underlining the proposed bioinspired design process, and as such, reviews existing literature to identify the opportunity, states the supporting evidence, and presents the approach for addressing the opportunity with an example and a case study to demonstrate applicability.

User requirements for analogical design support tools: Learning from practitioners of bio-inspired design

When designers develop biologically-inspired design (BID) solutions, they are engaging in a process of analogical design. Software tools have been developed to support analogical design processes, presenting designers with information to help in the construction of useful analogies. However, the requirements for such tools have not been explicitly informed by accounts of practitioners' experiences. To address this, interviews were conducted with 14 expert practitioners in BID to understand how they find and apply cross-domain analogies. Three main themes emerged from the analysis: (1) the skill sets of individual practitioners; (2) the ways they work as part of an interdisciplinary team; and (3) their orientations to biology. These themes present opportunities and challenges for developing analogical design support tools.

The impact of a relational mindset on information distortion

The preference-supporting bias in information evaluation, known as information distortion, is a ubiquitous phenomenon. The present work demonstrates that priming a relational mindset induces individuals to process independent units of information interdependently and therefore contributes to increasing distortion. In three studies, a relational mindset is activated by asking participants to generate solutions to cross-domain analogies. All three studies show that the activation of a relational mindset then carries over into a second, unrelated choice task and increases distortion. In addition, the present work shows that generating solutions to cross-domain analogies activates a high level of construal, which in turn mediates the effect of relational thinking on information distortion. Finally, the present work also demonstrates that imposing a cognitive load during the choice task reduces the impact of the relational mindset on distortion. In sum, this research demonstrates that the same mechanism that promotes creative thinking (i.e., seeing relationships across concepts) may also induce more biased information processing by prompting individuals to process independent units of information interdependently.

Product and Organism Aspects for Scalable Systematic Biologically-Inspired Design

Although Biologically-Inspired Design is gaining popularity, state-of-the-art approaches for systematic Biologically-Inspired Design are still limited by the required interactive work which is proportional to the applied biological database size. This interactive work, depending on the adopted methodology, might encompass model instantiation for each strategy in the biological database, classification into a predefined scheme, or extensive result filtering. To address this bottleneck, a scalable approach to systematic Biologically-Inspired Design, with the potential to leverage large numbers of biological strategies, is presented. Central to the approach are a set of Organism Aspects that form a conceptual representation of the biological domain. Inspired by the theory of inventive problem solving, this conceptual representation is an abstraction that facilitates knowledge reuse. The proposed Organism Aspects information structure supports a number of new systematic innovation functionalities aimed at assisting designers looking for bio-inspiration. Amongst these functionalities are the following: identification of a set of organisms relevant for the designer, a relevance sorted list of biological documents, and in-text annotation to facilitate cross-domain analogy identification.

Function Based Design-by-Analogy: A Functional Vector Approach to Analogical Search

Design-by-analogy is a powerful approach to augment traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. While the concept of design-by-analogy has been known for some time, few actual methods and tools exist to assist designers in systematically seeking and identifying analogies from general data sources, databases, or repositories, such as patent databases. A new method for extracting functional analogies from data sources has been developed to provide this capability, here based on a functional basis rather than form or conflict descriptions. Building on past research, we utilize a functional vector space model (VSM) to quantify analogous similarity of an idea's functionality. We quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. We also develop document parsing algorithms to reduce text descriptions of the data sources down to the key functions, for use in the functional similarity analysis and functional vector space modeling. To do this, we apply Zipf's law on word count order reduction to reduce the words within the documents down to the applicable functionally critical terms, thus providing a mapping process for function based search. The reduction of a document into functional analogous words enables the matching to novel ideas that are functionally similar, which can be customized various ways. This approach thereby provides relevant sources of design-by-analogy inspiration. As a verification of the approach, two original design problem case studies illustrate the distance range of analogical solutions that can be extracted. This range extends from very near-field, literal solutions to far-field cross-domain analogies.

Discovering Structure in Design Databases Through Functional and Surface Based Mapping

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.

Exploiting persistent mappings in cross-domain analogical learning of physical domains

Cross-domain analogies are a powerful method for learning new domains. This paper extends the Domain Transfer via Analogy (DTA) method with persistent mappings, correspondences between domains that are incrementally built up as a cognitive system gains experience with a new domain. DTA uses analogies between pairs of textbook example problems, or worked solutions, to create a domain mapping between a familiar and a new domain. This mapping enables the initialization of a new domain theory. Another analogy is then made between the domain theories themselves, providing additional conjectures about the new domain. After these conjectures are verified, the successful mappings are stored as persistent mappings to constrain future analogies between the domains. We show that DTA plus persistent mappings enables a Companion, the first structure mapping cognitive architecture, to learn the equation schemas and control knowledge necessary to solve problems in three domains (rotational mechanics, electricity, and heat) by analogy with linear mechanics. We provide a detailed analysis categorizing transfer failures. As with people, the most difficult step in cross-domain analogy is identifying an appropriate example. Once an analogous example has been found, DTA successfully transfers the domain knowledge necessary to solve the problem in the new domain 78% of the time. Furthermore, we illustrate how persistent mappings assist in retrieval of analogous examples and overcoming two types of mapping failures.

Cross-domain analogies as relating derived relations among two separate relational networks.

Contemporary behavior analytic research is making headway in analyzing analogy as the establishment of a relation of coordination among common types of trained or derived relations. Previous studies have been focused on within-domain analogy. The current study expands previous research by analyzing cross-domain analogy as relating relations among separate relational networks and by correlating participants' performance with a standard measure of analogical reasoning. In two experiments, adult participants first completed general intelligence and analogical reasoning tests. Subsequently, they were exposed to a computerized conditional discrimination training procedure designed to create two relational networks, each consisting of two 3-member equivalence classes. The critical test was a two-part analogical test in which participants had to relate combinatorial relations of coordination and distinction between the two relational networks. In Experiment 1, combinatorial relations for each network were individually tested prior to analogical testing, but in Experiment 2 they were not. Across both experiments, 65% of participants passed the analogical test on the first attempt. Moreover, results from the training procedure were strongly correlated with the standard measure of analogical reasoning.

A content account of creative analogies in biologically inspired design

AbstractThe growing movement of biologically inspired design is driven in part by the need for sustainable development and in part by the recognition that nature could be a source of innovation. Biologically inspired design by definition entails cross-domain analogies from biological systems to problems in engineering and other design domains. However, the practice of biologically inspired design at present typically isad hoc, with little systemization of either biological knowledge for the purposes of engineering design or the processes of transferring knowledge of biological designs to engineering problems. In this paper we present an intricate episode of biologically inspired engineering design that unfolded over an extended period of time. We then analyze our observations in terms ofwhy,what,how, andwhenquestions of analogy. This analysis contributes toward a content theory of creative analogies in the context of biologically inspired design.

Domain transfer via cross-domain analogy

Analogical learning has long been seen as a powerful way of extending the reach of one’s knowledge. We present the domain transfer via analogy (DTA) method for learning new domain theories via cross-domain analogy. Our model uses analogies between pairs of textbook example problems, or worked solutions, to create a domain mapping between a familiar and a new domain. This mapping allows us to initialize a new domain theory. After this initialization, another analogy is made between the domain theories themselves, providing additional conjectures about the new domain. We present two experiments in which our model learns rotational kinematics by an analogy with translational kinematics, and vice versa. These learning rates outperform those from a version of the system that is incrementally given the correct domain theory.

From design experiences to generic mechanisms: Model-based learning in analogical design

AbstractAnalogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.