Some historical and current trends in reflective practice (RP), artificial intelligence (AI), and engineering design (ED) are presented and compared. Human artistry, context, and connectionist approaches to knowledge are the common threads highlighted. ED is considered to be a type of RP and AI a part of RP. This is supported by an analysis of the transformation processes involved in each. AI and systems are presented as approaches for the formalization of RP at the technical and conceptual levels, respectively. Interconnectedness in a hierarchical fashion and purposeful process loops are defined as the key ingredients of a systems approach. AI techniques that could support a range of ED categories (case-based reasoning, decomposition, and transformation) are identified, as are the wider RP approaches that subsume those categories. The ED, AI, and RP categories are identified as spanning from routine to creative, connectionist to cognitivist, and intuitive to deliberate, respectively.

KANT is a knowledge-based system designed to diagnose Olivetti personal computers connected as remote terminals to a host computer through a SNA link. KANT is a collection of a few knowledge-based units: some of them operate in the field, and some operate back at the home office. They are configured in two blackboard systems which exchange data via the SNA link. The first blackboard runs on a cheap personal computer, only employs shallow knowledge, and performs the diagnoses that can be achieved in the field. The second blackboard runs on corporate mainframes, employs deep knowledge, and supports the more sophisticated analysis that is required from the project team for fixing complex problems.

A crucial early stage in the engineering design process is the conceptual design phase, during which an initial solution design is generated. The quality of this initial design has a great bearing on the quality and success of the produced artefact. Typically, the knowledge required to perform this task is only acquired through many years of experience, and so is often at a premium. This has led to a number of attempts to automate this phase using intelligent computer systems. However, the knowledge of how to generate designs has proved difficult to acquire directly from human experts, and as a result, is often unsatisfactory in these systems. The application of inductive machine learning techniques to the acquisition of this sort of knowledge has been advocated as one approach to overcoming the difficulties surrounding its capture. Rather than acquiring the knowledge from human experts, the knowledge would be inferred automatically from a set of examples of the design process. This paper describes the authors' investigations into the general viability of this approach in the context of one particular conceptual design task, that of the design of fluid power circuits. The analysis of a series of experiments highlights a number of issues that would seem to arise regardless of the working domain or particular machine learning algorithm used. These issues, presented and discussed here, cast serious doubts upon the practicality of such an approach to knowledge acquisition, given the current state of the art.

Intelligent agents must update their knowledge base as they acquire new information about their environment. The modal logic S5n has been designed for representing knowledge bases in societies of agents. Halpern and Vardi have proposed the notion of refinement of S5n Kripke models in order to solve multi-agent problems in which knowledge evolves. We argue that there are some problems with their proposal and attempt to solve them by moving from Kripke models to their corresponding trees. We define refinement of a tree with a formula, show some properties of the notion, and illustrate with the muddy children puzzle. We show how some diagnosis problems in engineering can be modelled as knowledge-based multi-agent systems, and hence how our approach can address them.

In many system designs, it is a challenging task for system designers to predict the system reliability due to limited information about component designs, which is often proprietary to component suppliers. This research addresses this issue by considering the following situation: all the components share the same system load, and system designers know component reliabilities with respect to the component load, but do not know other information, such as component limit-state functions. The strategy is to reconstruct the equivalent component limit-state functions during the system design stage such that they can accurately reproduce component reliabilities. Because the system load is a common factor shared by all the reconstructed component limit-state functions, the component dependence can be captured implicitly. As a result, more accurate system reliability can be produced compared with traditional methods. An engineering example demonstrates the feasibility of the new system reliability method.

Artificial neural networks are finding wide application to a variety of problems in civil engineering. This paper describes how artificial neural networks can be applied in the area of construction project control. A project control system capable of predicting and monitoring project performance (e.g., cost variance and schedule variance) based on observations made from the project environment is described. This project control system has five neural network modules that allow a project manager to automatically generate revised project plans at regular intervals during the progress of the project. These five modules are similar in design and implementation. Therefore, this paper will present the main issues involved in the development of one of these five neural network modules, that is, the module for identifying schedule variance. A description of a graphical user interface integrating the neural network modules developed with project management software, and a discussion on the power and limitations of the overall system conclude the paper.

This article focuses on a shape grammar, the rabo-de-bacalhau housing style, that was developed to enable the adaptation of existing houses to new requirements and most particular on the process of inference of the grammar. In the article we describe the process undertaken to develop the grammar and what the achievements of the transformation grammar are regarding the possibilities of a mass customization of a dwelling's rehabilitation work. The goal of this article is to describe and discuss how the designer's knowledge was encoded into shape rules. The process used to extract the architect's knowledge and to incorporate it into the transformation grammar enable us to abstract the designer's actions and to define a sequence of actions that can define a possible strategy of design. The proposed design methodology generates dwelling layouts that are legal because they follow the grammar language and adequate because they meet the a priori user and design requirements.

This article contains a description of a constraint solver that determines complete solution spaces. These spaces are defined by sets of constraints in continuous variables. Interactive design is supported through improvements to existing algorithms that have increased system performance. In addition, heuristics for activating the best set of preferred constraints for a design task are presented, and two ways are proposed for interactively exploring design alternatives. IDIOM is an application framework that has successfully tested these algorithms for interactive apartment layout design.

The exponential growth of the Internet and increasing communication and computational power have created many opportunities for advancing engineering, manufacturing, and business activities. Among them are electronic catalogs. These have become basic information resources to a number of people, ranging from shoppers looking for personal items to engineers selecting electromechanical parts to build a product. Although rich in content, current catalog systems are limited both in search quality and in realizing the full potential of the retrieved information. The active catalog system brings a conceptually new idea to electronic commerce by providing a new, computationally usable, catalog information environment about components and their use in applications. It utilizes a rich body of domain knowledge to facilitate access and retrieval of component information. The utility of retrieved information is enhanced by using it to rapidly construct simulation programs and test alternatives, supporting a “try before you buy” paradigm in which users evaluate candidate components within simulations of their design. We describe services provided in the active catalog system to support engineers in selecting and evaluating electromechanical components and subsystems. The services include mechanisms for creating queries for parts based on their intended use rather than merely parametric specifications, refining those queries to take account of constraints imposed by domain knowledge, providing multimodal information to help engineers assess and compare candidate parts, and generating simulation models for candidate parts and integrating them to provide simulation models for candidate systems.

The problem investigated in this research is that engineering design decision making can be complicated and made difficult by highly coupled design parameters and the vast number of design parameters. This complication often hinders the full exploration of a design solution space in order to generate optimal design solution. These hindrances result in inferior or unfit design solutions generated for a given design problem due to a lack of understanding of both the problem and the solution space. This research introduces a computational framework of a new algebraic constraint-based design approach aimed at providing a deeper understanding of the design problem and enabling the designers to gain insights to the dynamic solution space and the problem. This will enable designers to make informed decisions based on the insights derived from parameter relationships extracted. This paper also describes an enhanced understanding of an engineering design process as a constraint centered design. It argues that with more effort and appreciation of the benefits derived from this constraint-based design approach, engineering design can be advanced significantly by first generating a more quantitative product design specification and then using these quantitative statements as the basis for constraint-based rigorous design. The approach has been investigated in the context of whole product life-cycle design and multidisciplinary design, aiming to derive a generic constraint-based design approach that can cope with life-cycle design and different engineering disciplines. A prototype system has been implemented based on a constraint-based system architecture. The paper gives details of the constraint-based design process through illustrating a worked real design example. The successful application of the approach in two highly coupled engineering design problems and the evaluation undertaken by a group of experienced designers show that the approach does provide the designers with insights for better exploration, enabled by the algebraic constraint solver. The approach thus provides a significant step towards fuller scale constraint-based scientific design.

High speed machinery has played and continues to play a vital role in the manufacture and production of consumer goods. In the design of high speed systems there are two key considerations: power transmission and motion control. Although there is considerable computer-based support for the design of systems to achieve requirements of power transmission, there is only limited support for the design of systems to deliver complex motion control. This is particularly the case where mechanism and linkage systems are considered in order to achieve large displacements and intricate paths involving reentrant and reciprocating components. One explanation for this relative lack of supportive tools is the underlying reasoning and analysis techniques implemented within many commercial and research software environments. To overcome these limitations a constraint-based approach has been employed to provide the fundamental elements of a design environment for mechanisms and machine systems. The design environment provides support for the transition from concept to embodiment stages of the design process and the subsequent stages of detailed design and optimization. In contrast to many research approaches the design environment presented in this paper has been created and developed through close collaboration with industry and through extensive application to real design scenarios. First, the underlying representations and methods are presented. The fundamental elements of the design environment are then described and its capabilities discussed with particular reference to the use of constraints in design.

Many of the design systems developed in recent years incorporate some machine learning. The number of such systems already available, and the multitude of design learning opportunities that are slowly being revealed, suggest that the time is ripe to attempt to put these developments into a systematic framework. Consequently, in this paper we present a set of dimensions for machine learning in design research. We hope that it can be used as a guide for comparing existing work, and that it may suggest new directions for future exploration in this area.

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.

Knowledge management has recently become the focus of public attention in business and engineering. Because knowledge acquisition is situated in the upstream of knowledge management, capturing knowledge is an important step for enterprises to achieve successful knowledge management. We focus on how engineers solve their design problems under given design contexts and propose a novel model and methods to capture knowledge from engineering design processes. Our goal is to acquire know-how knowledge without disrupting the normal design process. A three-layer design process model is introduced to represent generic design processes, and a grammar and extended dynamic programming (GEDP) method is developed based on the process model. GEDP adopts the grammar approach and EDP to automatically identify meaningful clusters, called operations, from primitive design events. Our approach is evaluated through a case study of designing a double-reduction gear system.

This paper reports on the results of an analysis of existing ontological systems to determine which is most appropriate for the manufacturing domain. In particular, this involved the exploration of efforts that are studying both the uses of ontologies in the general sense and those that are using ontologies for domain-specific purposes. Eleven ontological systems were analyzed and, using a set of analysis criteria, it was determined that the Cyc (Cyc is a registered trademark of Cycorp Inc.) system was most appropriate for modeling concepts in the manufacturing domain. After the analysis is described, examples are given to show how manufacturing concepts could be modeled in the Cyc system. This work is part of a larger project whose objective is to move closer to the ultimate goal of seamless manufacturing systems integration using the principle behind ontological engineering to unambiguously define domain-specific concepts. The output of this work will be a taxonomy of manufacturing terms and concepts along with formal definitions of exactly what each of those terms and concepts mean and how they interrelate.

Much of preliminary engineering design is a constraint-driven non-monotonic exploration process. Initial decisions are made when information is incomplete and many goals are contradictory. Such conditions are present regardless of whether one or several designers contribute to designs. This paper presents an approach for supporting decisions in situations of incomplete and conflicting knowledge. In particular, we use assumptions and conflict management to achieve efficient search in contexts where little reliable information exists. A knowledge representation, containing a semantic differentiation between two types of assumptions, is used within a computational model based on the dynamic constraint satisfaction paradigm. Conflict management strategies consist of three generic mechanisms adapted to the type of constraints involved. These strategies may be refined through consideration of variable importance, context, and design inertia.

Conceptual design is a key stage of product design and has received increasing attention in recent years. However, this stage is characterized by limited information, large uncertainty, and multidisciplinary aspects. Thus, increased workload and time cost are associated with conceptual design information acquisition; sometimes, it is difficult to develop novel solutions and the feasibility of the solutions obtained according to these limited and uncertain information is difficult to guarantee. Genetics-based design (GBD) is an effective approach to develop novel solutions and improve the reuse of knowledge, which is consistent with the goal of the conceptual design process. Product-gene acquisition is the premise and basis of GBD. At present, there are few reported studies in this area; most of the existing works are constrained by the structural aspects of the acquisition process, and there are limited studies on specific implementation techniques. To explore the specific implementation technologies of product-gene acquisition, an intelligent acquisition method based on K-means clustering and mutual information-based feature selection algorithm is proposed in this paper. The product genes defined in this paper are key product information that determines the nature of the product and influences the conceptual design process. Thus, solutions obtained according to them are more feasible than that based on limited and uncertain information. An illustrative example is presented. The results show that the proposed method can achieve intelligent acquisition of product genes to a certain extent. Further, the proposed method will allow designers to quickly search for the corresponding product genes when performing similar functional design tasks.

The use of grammars in design and analysis has been set back by the lack of automated ways to induce them from arbitrarily structured datasets. Machine translation methods provide a construct for inducing grammars from coded data which have been extended to be used for design through pre-coded design data. This work introduces a four-step process for inducing grammars from un-coded structured datasets which can constitute a wide variety of data types, including many used in the design. The method includes: (1) extracting objects from the data, (2) forming structures from objects, (3) expanding structures into rules based on frequency, and (4) finding rule similarities that lead to consolidation or abstraction. To evaluate this method, grammars are induced from generated data, architectural layouts and three-dimensional design models to demonstrate that this method offers usable grammars automatically which are functionally similar to grammars produced by hand.

This article poses the notion that it is possible and desirable to formalize and apply design critiques in a specialized framework. It describes GENCRIT, (GENeric CRItiquing Tool), one such framework for design critiquing. The article starts by highlighting the role of critics in the design process. It then goes on to bring out the need for a critic building tool, viz. that of aiding in the rapid development of multiple critics. GENCRIT combines knowledge-based techniques and a multifactor decision making model to develop an integrated approach to evaluation that encompasses a wide range of designs. Critics developed using GENCRIT evaluate candidate designs based on the critiquing knowledge provided by experts, give justifications for the evaluation, and suggest improvements. The working of GENCRIT is illustrated with two examples: a constructibility critic for reinforced concrete buildings and a bridge design critic.