Automatic conceptual design using experience-derived heuristics

Potter, Stephen; Culley, S J; Darlington, Mansur; Chawdhry, Pravir

doi:10.1007/s00163-003-0034-4

Cited by 24 publications

(9 citation statements)

References 26 publications

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“…There are two options at this point: it can either be explicitly provided with domain rules or it can use previous data as a means for model fitting to the domain. Providing the domain rules explicitly requires these rules to be acquired, this represents an expensive approach in terms of the time required by a knowledge engineering to extract these rules (Potter et al, 2001(Potter et al, , 2003. The alternative approach is to use machine learning algorithms to fit domain models based on prior design examples.…”

Section: Discussionmentioning

confidence: 99%

Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

Matthews

2010

Res Eng Design

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Section: Discussionmentioning

confidence: 99%

Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

Matthews

2010

Res Eng Design

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“…To that end, several systems have been developed to store the design knowledge contained in the functional models for design reuse [21][22][23]. In addition, computational tools have been developed to exploit the knowledge contained in the design repositories for the purpose of concept variant generation [24][25][26]. These works lend credence to the assertion that functional modeling is a useful engineering language for indexing design concepts.…”

Section: Functional Modelingmentioning

confidence: 98%

Facilitating Design-by-Analogy: Development of a Complete Functional Vocabulary and Functional Vector Approach to Analogical Search

Murphy

Otto

et al. 2014

Volume 2A: 40th Design Automation Conference

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Design-by-analogy is an effective approach to innovative concept generation, but can be elusive at times due to the fact that few 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 analogies from data sources has been developed to provide this capability. Building on past research, we utilize a functional vector space model to quantify analogous similarity between a design problem and the data source of potential analogies. We quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. We develop a complete functional vocabulary to map the patent database to applicable functionally critical terms, using document parsing algorithms to reduce text descriptions of the data sources down to the key functions, and applying Zipf's law on word count order reduction to reduce the words within the documents. The reduction of a document (in this case a patent) into functional analogous words enables the matching to novel ideas that are functionally similar, which can be customized in various ways. This approach thereby provides relevant sources of design-by-analogy inspiration. Although our implementation of the technique focuses on functional descriptions of patents and the mapping of these functions to those of the design problem, resulting in a set of analogies, we believe that this technique is applicable to other analogy data sources as well. As a verification of the approach, an original design problem for an automated window washer illustrates the distance range of analogical solutions that can be extracted, extending from very near-field, literal solutions to far-field cross-domain analogies. Finally, a comparison with a current patent search tool is performed to draw a contrast to the status quo and evaluate the effectiveness of this work.

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“…Heuristics have also been identified as an effective mechanism for capturing not just design knowledge but the design knowledge of experts [11]. Developing a formal framework for capturing heuristics that can aid in the design of computer-based systems, and specifically with respect to architecture, has been a focus of our research [8,9,10].…”

Section: Figure 2 Architectural Optimisation Frameworkmentioning

confidence: 99%

Utilising Abstract Matching to Preserve the Nature of Heuristics in Design Optimisation

Maxwell

Leaney

O'Neill³

2008

15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (Ecbs 2008)

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Design space exploration, the generation of alternate designs to identify working designs with varying system properties, has the potential to provide a basis for the optimisation of computer-based system architectures. To utilise design space exploration for this purpose requires that an effective mechanism exist for the storage and application of potential design changes. Heuristics have shown some promise in this area due to their ability to capture expert design knowledge and their flexibility across multiple domains. Heuristics are also especially attractive as change descriptions as they can capture changes that operate across a large spectrum of change detail, from the very detailed to the very abstract. Heuristics are, however, at their most powerful, and their most useful, when they are specified in an abstract manner. This presents a challenge in the formal application of heuristics in capturing design knowledge. Formally describing heuristics inclines their specification of change to be in a more detailed, more concrete, state than an abstract one. This occurs because architectural models tend be both domain specific and are often described at a more concrete level than the level at which the heuristic is described. This has the potential to greatly reduce the effectiveness of heuristics. In this paper we propose that by providing an abstract match method heuristics may be specified in an abstract manner and still be applied to a detailed formal model, thereby eliminating this problem.

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Automatic conceptual design using experience-derived heuristics

Cited by 24 publications

References 26 publications

Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

Facilitating Design-by-Analogy: Development of a Complete Functional Vocabulary and Functional Vector Approach to Analogical Search

Utilising Abstract Matching to Preserve the Nature of Heuristics in Design Optimisation

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