Design Support Using Distributed Web-Based AI Tools ... - Springer Link

Research in Engineering Design (1999)11:31–44 ß 1999 Springer-Verlag London Limited

Research in

Engineering Design

Design Support Using Distributed Web-Based AI Tools Paul A. Rodgers1, Avon P. Huxor2 and Nicholas H. M. Caldwell3 1

Engineering Design Centre, Department of Engineering, University of Cambridge, Cambridge; 2Centre for Electronic Arts, Middlesex University, Barnet; 3Scientific Image Processing Group, Department of Engineering, University of Cambridge, Cambridge UK

Abstract. Currently, designers are faced with searching through a ‘sea’ of on-line knowledge to support their decision making activities. This paper describes WebCADET, which is a reimplementation of the stand-alone CADET – a KnowledgeBased System (KBS) for product design evaluation. WebCADET aims to provide effective and efficient support for designers during their searches for design knowledge. WebCADET uses the ‘AI as text’ approach, where KBSs can be seen as a medium to facilitate the communication of design knowledge between designers. The development of WebCADET to include practical support via World Wide Webbased functionality, which illustrates the potential of the ‘AI as text’ approach, is described in the paper.

Keywords: AI as text; Design knowledge; Design support; Distributed design

Many design issues currently require the designer not only to be technically (or technologically) competent, but to have a well developed sense of, for example, aesthetic appreciation, including an awareness of spatial composition, form, line, colour and texture issues (Lawson 1990). Given the ‘soft’ nature inherent in many design activities, especially in areas such as product, graphic and architectural design, AI strategies have been seen as particularly appropriate for the provision of computer support. Many of the issues that arise in design areas such as product design and architectural design, are of a subjective nature, are difficult to quantify, and therefore the heuristic nature of AI and KBS technology offers a better means of representing such knowledge.

1. Introduction Design has been widely described as both an illstructured activity (Rowe 1987; Cross 1994), in that it lacks a well defined objective, and also as one of the most demanding of all human activities (Gero 1990). Designers are responsible for shaping, or changing, physical aspects of society, the objective being to improve the lives of people through ‘better designed’ houses, roads, products, transportation systems, etc. (Coyne et al. 1990). It is sometimes common in design for certain features of the problem to be understood – through a design specification or through market research – however, typically, there will be no one unique solution (Pugh 1991). Different designers will not only develop, and concentrate on, different parts of the problem but will, almost certainly, arrive at different solutions – each with their own strengths and weaknesses (Jones 1970). Correspondence and offprint requests to: P. A. Rodgers, Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK. Email: [email protected]

1.1. Design Knowledge Knowledge is a difficult concept to define accurately and the term is often used to cover terms such as ‘information’, ‘data’, ‘understanding’ and even ‘experience’ (Marsh 1997). Hubka and Eder (1996) define ‘knowledge’ as that which is held in a person’s memory maps, and ‘information’ as everything that is recorded external to the human mind. This paper will use the term ‘knowledge’ to include both ‘knowledge’ and ‘information’ as defined by Hubka and Eder (1996). Contemporary design problems embody significant levels of complexity which make it unlikely that a ‘single’ designer will work alone on a design problem. The continuing growth of knowledge and supporting information and ever increasing complexity of design problems has led to increasing specialization. This is not a new problem. Over thirty years ago Alexander (1964) warned:

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P. A. Rodgers et al.

Fig. 1. Knowledge areas for design.

. . . information (design information) is hard to handle; it is widespread, diffuse, unorganised Moreover, the quantity of information is now well beyond the reach of single designers. . . There is a large body of knowledge that designers call upon and use during the design process to match the ever increasing complexity of design problems (Ullman 1997). Typically, designers initially require broad, shallow knowledge to understand and analyze the problem, and to aid concept design development, materials selection, the specification of manufacturing processes, and so on (Nowack 1997). Later in the design process, which is both non-trivial and nonlinear, the knowledge generated will be used for testing the performance of candidate solutions. The provision of timely and relevant knowledge to designers during the process is vital to the successful development of the product or system being designed, and to the future competitiveness of the company involved (Smith and Reinertson 1991; Court et al. 1997). Moreover, design problems today require knowledge in many areas including ergonomics, packaging, management, manufacturing processes and so on (Fig. 1). 1.1.1. Design Knowledge Access and Retrieval Given that even the most routine of design tasks is dependent upon vast amounts of expert design knowledge, there is a need for some sort of support. The acknowledgement of this problem has resulted in a number of recent studies being undertaken in this area. The key focus for much of this work is the way in which designers capture, manage, access and retrieve knowledge throughout the design process (Blessing 1993; Bradley et al. 1994; Court et al. 1995; Wood III and Agogino 1996; Dong and Agogino 1996; Schott et al. 1997).

Fig. 2. Designers’ initial point of contact when searching for knowledge.

During the design process, it has been estimated that designers spend anything between 30–40% of their time searching for and locating the ‘right’ knowledge (Cave and Noble 1986; Marsh 1997). Support (computer-based or otherwise) is required, therefore, to free designers from much of the drudgery involved in searching and locating relevant knowledge, so that they can concentrate on the more demanding and important activities involved in product design and development. In a recent study of the knowledge requirements by design teams in a large telecommunications company in the United Kingdom (Rodgers 1997), designers were found to make the following initial points of contact when searching for design knowledge (Fig. 2). It is noticeable from this figure that the designers’ initial point of contact when looking for knowledge is to search a file. In this context, ‘file’ relates to both external and internal documents (predominately electronic-based in the case study undertaken). The knowledge sought by the designers included market research data, product costs, ergonomic information, as well as internal technical memos and so on. External documents would include documents such as ISO (International Standards Organisation) documents or contractors’ design knowledge. The second most popular means of getting knowledge is by consulting either ‘external personnel’, that is external personnel to the design unit of the company involved, or ‘internal personnel’, such as another member of the design team. The main media found to be used by designers when searching and supplying knowledge during this study are illustrated in Fig. 3.

Design Support Using Distributed Web-Based AI Tools

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Fig. 3. Media used when searching and supplying knowledge (in an average week).

Here, one can see that email is by far the heaviest used medium. Indeed, it is used almost twice as much as the next popular form of communication – the telephone. The Internet and intranets are the next most popular options. Other ‘more traditional’ methods of communication, such as face-to-face meetings and fax, were not found to be used nearly as much as they are in other design scenarios, such as those described by Court et al. (1993), Harmer (1996) and Marsh (1997). This may be due to the fact that the designers involved in this study feel comfortable using the latest forms of communication technologies, and that they were readily available. On the other hand, it may be a reflection of the general diffusion of Internet and intranet use in many design activities of new product development (Malinen and Salminen 1997). Indeed, conservative estimates predict that by the year 2000 as many as 200 million people will be on-line (Table 1). Others say one billion (Thackara 1997). Therefore, the role of the Internet in design practice is likely to be very significant in the future. Designers thus access knowledge in two main ways: either through written material, or through colleagues, who possess the knowledge themselves or are able to direct the enquirer towards the appropriate knowledge source. It is noticeable from Fig. 2 that human contacts still provide the majority of knowledge to designers. This is primarily due to their Table 1. World-wide connectivity market 1996–2000 (millions of users-future years forecast) Year

1996

On-line Users 60

1997

1998

1999

2000

80

130

180

200

colleagues’ expertise which has been acquired through many years of experience and working practice, and also because of the ease at which the information can be ‘accessed’ through simple conversation. In summary, the findings from this study correspond to that from other studies in the area of design knowledge and information retrieval (Kuffner and Ullman 1991; Marsh 1997). That is, designers rely heavily upon knowledge from their colleagues and from knowledge that is stored electronically (e.g. in databases, electronic reports, drawings, etc.) which are derived from more conventional paper-based materials. This is likely to hold true for many design scenarios, as is the fact that large amounts of the knowledge that designers require access to is stored externally from their place of work. Therefore, a common framework is required that will enable designers to capture, store and retrieve knowledge efficiently and effectively throughout the design process.

1.2. Collaborative and Virtual Organizations The role of social assistance in knowledge access, however, meets a problem when we move toward emerging distributed and virtual organizations. These new forms of working are being made possible by the communication technologies in general, and the Internet in particular. For example, Object Services and Consulting Inc., is a company structured as a virtual office (http://www.objs.com/survey/vo.htm). As of August 1997, it had eight full-time employees and a part-time office manager, who were spread across six geographic regions of the USA. All reported to ‘work’ from their home offices. Although many of the pioneers of this form of working are, as one might expect, companies specializing in knowledge and communication technology products and services, it is likely that, like email, such tools will diffuse into the wider commercial world, including design. The move to distributed working and virtual organisations which are brought together only for certain projects may create certain problems, however. Clearly, if the designer does not meet his or her colleagues socially then the opportunity for casual knowledge exchange is reduced, and expertise may be lost as the team breaks up to work on new projects with new partners. Although seemingly unimportant, the chance encounters that occur in workplaces have been shown to be crucial to the success of projects in both research and design work-places (Hillier 1996).

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The role of the expertise of colleagues in locating knowledge raises the issue of how such expertise can be made available and preserved in a distributed, ever-changing work-team. Expert systems, as ‘embodiments of expertise’ seem to offer a potential solution. Such agents can assist users in locating domain specific knowledge. The remaining part of this paper concerns how expert systems technology can be used to assist in navigating knowledge present on the WWW, drawing on an alternative view of KBSs.

2. CADET as a Stand-Alone Solution In many cases of design – particularly in the design of domestic consumer products such as kitchen appliances, audio and video equipment, furniture and automobiles – the concern is not with producing completely radically different solutions but with modifying, in an attempt to improve upon, certain elements of the existing product’s performance (Hubka and Eder 1996). This type of design is sometimes referred to as variant design (Dym 1994). Typically, in variant design understanding of the sources of design knowledge is known, but complete understanding of how that knowledge should be applied is lacking. For example, although we may be able to successfully decompose the task of designing an automobile into a series of manageable sub-tasks of component design (e.g. wheels, transmission

Fig. 4. CADET support in design (after French, 1985).

systems, etc.), it is the modification and combination of the individual components that makes successful total automobile design difficult. With this in mind, the original stand-alone CADET (Computer-Aided Design Evaluation Tool) system was developed to support designers during the early conceptual stages of design (Rodgers 1995). Figure 4 illustrates the activities (shaded areas) which CADET supports. The first prototype of CADET was developed in close collaboration with several designers from six small to medium-sized design enterprises (SMEs) in the central London area, where the goal was to support designers during their generation, evaluation and selection of concept designs. From observations of the designers at work, it was found that designers generate many sketch-based ideas when working on a design problem (Fig. 5). The process of generating sketch-based solutions and checking them against the stated problem or requirements helps to develop not only the solution, but it can also provide new and valuable insights into the problem. CADET supports this by providing a structure for defining, constructing, recording and indexing product attributes (statements of the design problem) in users’ terms, and by providing rapid feedback on the individual merits of the concept designs proposed. This procedure is not intended to be exhaustive but its value is that it is quick and effective in providing a guide for the relative strengths and weaknesses between concept designs. The original CADET system comprised a number of product attributes which were defined through a range of methods including user interviews, questionnaires, and from ‘past’ projects. Specifically, 80 individuals (with a good spread of ages, gender and background) were given a number of different examples of each of three product classes (toothbrush, mobile phone, shaver), one at a time, and asked to critique them by listing what they liked and disliked about each one. This is similar to Alexander’s method of identifying ‘misfit’ (Alexander 1964). This

Fig. 5. Concept sketches for a new disposable shaver.


Fig. 6. CADET explanation facility.

exercise was followed up by a questionnaire session where individuals were asked to formally record their likes and dislikes of the three product classes. Lastly, attributes that had been previously identified in consumer reports, trade journals and company knowledge from ‘past’ projects were used in defining a set of product attributes for each product class. A fuller account of this work can be found elsewhere (Rodgers 1995). The product attributes help provide an initial focus for the designer and help structure the (potentially enormous) design problem. CADET contains a large resource of expert design knowledge. This knowledge is utilized in two ways. First, the knowledge is used in the construction of the product attribute models. Secondly, the knowledge is explicitly used in CADET’s explanation facility (Fig. 6) as justification for the concept proposals’ evaluation results. Gosbee and Ritchie suggest that the facility for providing explanation is an essential requirement of any computer-based tool (Gosbee and Ritchie 1997). To summarize, CADET aims mainly to support design activities commonly described as variant design. This includes the design of products such as domestic appliances, audio-video products and furniture where there is gradual evolution in the appearance and performance of the product. This type of design encompasses a substantial knowledge base. The majority of this knowledge, however, exists for technical or ‘scientific’ aspects of the design as opposed to the visual or ‘styling’ aspects.

2.1. CADET as ‘Text’ The authors’ recent work on CADET (Rodgers and Huxor 1996; Rodgers and Huxor 1998) has been based on a re-conceptualization of the system, drawn

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from the earlier work of Huxor (1994). This work argued that symbolic AI, such as that employed in CADET, can be better understood as a form of, and extension to, the written word. That is, the author of the KBS is like a traditional author, but they create a dynamic and computational ‘text’, rather than create models of real minds. The textual nature of CADET is best illustrated in the explanation facility, mentioned above. This is a standard component in any KBS, which displays the line of reasoning that the system employed to achieve its conclusions. It is not difficult to imagine that one might, for example, use the system as a means to create a detailed report on a specific product after it has been designed The line of reasoning used in CADET’s explanation facility does not automatically generate a printed report, but it does offer some support in this task. These reports could then be saved and stored as part of the design records so that they may be used in future projects. Recording and storing detailed design reports for future reference is obviously an attractive feature for designers (Blessing 1996; Yang and Cutkosky 1997). Used in this manner, the system can be understood as a structured, computational, text-editor. That is, just as current word processors often include spell-checkers and thesaurus to give the user access to conventionalized forms of spelling and meaning, so CADET would act as a means to present the user with standard forms of design knowledge in the given domain. The approach goes further, however, and argues that CADET itself can be viewed not just as a means to create explanation ‘texts’, but that the whole KBS itself is a ‘text’, authored by its builders. The rule base is made visible to the users, and if they disagree they can modify it to create their own ‘authored’ version. For example, one designer may from their own experience consider that design data regarding tool handle dimensions and geometry should have different values from that suggested in the original CADET rule version. There is a parallel here with mathematically computational authoring systems, such as Mathematica, except that the programs inherent in the ‘text’ are derived from work in KBS, rather than mathematics. This ‘AI as text’ view also brings KBSs into the more mainstream arenas of design rationale capture and organizational memory. These are primarily dependent upon more conventional written records. Just as a spreadsheet model can act as a computational record of aspects of a product’s development, so the symbolic model within CADET documents appropriate design decisions.

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In summary, there are a number of reasons why the ‘AI as text’ approach has advantages over the conventional approach in which the system is experienced by the user as an autonomous ‘agent’ and the user interface is of a conversational mode. The ‘AI as text’ view explicitly locates a KBS with an author (or authors), and hence provides a social and cultural context. The view of this paper is that in many design domains, and in many tasks in design, in particular, this context setting is crucial. Also, the problem arises in the more conventional approach in that there is a tendency for the user to assume that the machine capabilities are greater than is actually the case. Even when they function well, there is a problem of user acceptance, as users often object to the locus of control moving to another agent for decisions which they will be held responsible. For example, it was demonstrated experimentally that MYCIN performed diagnosis at the level of a human doctor, but the system was eventually abandoned and was never employed in a practical medical setting (Lipscombe 1989).

2.2. Distributed CADET In many design domains, particularly in product design, graphic design and architectural design, there are a number of elements involved which can be categorized as ‘subjective’ (Roozenberg and Eekels 1995). This means that it is difficult, if not impossible, to formulate rules that will be universally applicable as there are elements inherent within design such as colour, texture and shape which are of a subjective nature (Glaze et al. 1996). Moreover, in design areas where there exists high levels of human interaction, particularly in the design of many domestic consumer products (e.g. kitchen appliances, furniture, audiovisual products, etc.), it is difficult to formulate universal rules because of, for example, national/ regional differences in anthropometric data, differences in the backgrounds, education and abilities of individuals and also because of regional/local cultural expectations and requirements (Norman 1989; Nicholson 1991; Ito 1995; Hubka and Eder 1996). Research suggests, therefore, that there is a need to provide computer support that will supply clear and complete design knowledge, and also facilitate designer intervention and customization during the decision-making activities in the design process (Madni 1988; Fischer and Nakakoji 1992; Sanders and McCormick 1992). The application of the ‘AI as text’ approach, proposed here, means that modifications and versions


Fig. 7. CADET rule evolution.

of CADET ‘texts’ can be made available to the design community at large. That is, implicit within this approach is that a market in explicitly authored ‘text’ versions would be created (Fig. 7). Some CADET ‘texts’ would be more highly considered than others, with some being more localized for certain markets. However, the user (designer) will be in a position to choose according to provenance, amongst other considerations such as the reputation of the ‘author’, the applicability of the ‘context’, and the ‘date’ the rule was written. This approach makes this process clearer, as it does not view the system as an autonomous intelligent system which would imply a conversational interface in which the user must teach the system new facts. Instead it is a ‘text’ which the user simply edits (although as in conventional writing, it is necessary to learn the writing system). 2.2.1. CADET Extension to Exploit WWW Functionality (WebCADET) The Internet (and the World Wide Web, in particular) is seen as a natural medium for the establishment of a market in KBS design knowledge (Storath et al. 1997). Currently, WebCADET acts as a repository for the accumulation and communication of design knowledge. This knowledge is in a rule-based form, similar to the stand-alone CADET’s rule-based attribute models. Designers will be able to browse this facility via existing Web browsers, and download the knowledge to their host machines. In turn, designers will be able to critique the KBS rules and to re-submit any modifications they have made to suit their own particular scenario or task back to the design community via the Web (as suggested in Fig. 7). The hope is that a ‘rich’ and comprehensive market of design knowledge, mainly in the form of


KBS rules, will quickly be established. The reason for extending the original CADET, both theoretically and practically, by placing it on the WWW as a distributed resource is motivated by two main reasons: . Firstly, there is a need for a mechanism to allow for the reading, writing and distribution of these dynamic ‘texts’. The Internet provides two useful features that will encourage this. One is the large and growing number of users, and the other is the encouragement of standards inherent in the WWW, allowing for cross-platform functionality. . Secondly, as described in Section 1.2, a future is envisaged in which there are distributed, virtual, design teams. Their members are brought together for certain projects, and would frequently work over the networks. Part of the value of the Internet is in its total transcendence of geography. For example, a designer who downloads the latest design knowledge need not concern him or herself with whether the computer holding this data is 10 miles or 10,000 miles away. All knowledge available in any computer connected to the Internet is, in theory, stored everywhere. Consequently, this means that designers and engineers can now work on the same design from very different geographic locations that was not possible, or at least very difficult, previously. Originally, the intention for placing CADET on the WWW (WebCADET) was due to the need for a mechanism which would allow designers to access knowledge-bases, their supporting references, and to permit these to be modified as required and subsequently made public for other users, as outlined in Fig. 7. However, it quickly became clear that WebCADET created the possibility of supporting designers in the navigation of on-line knowledge, in addition to other navigation and search tools available. This relies on an extension of the referencing system within CADET illustrated earlier in Fig. 6. The referencing system within CADET’s explanation facility provides justification to the designer for results derived during concept evaluation. For example, Fig. 6 illustrates part of the explanation of why a particular shaver design concept would not satisfy the shaver attribute gives_a_close_shave. One of the reference sources used in this decision is Terry (1991). This reference source reflects expert opinion and states that because the shaver design concept did not have a space of at least 1.5 mm between the first and second blades of the shaver then it would not give a close shave. As mentioned earlier and illustrated in Fig. 7, however, it is likely that there are as many

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published technical reports or expert opinions that disagree with this point as support it. The ‘AI as text’ approach, however, facilitates different points of view by encouraging authors of rules (designers) to submit their rule-base versions with accompanying reference material to WebCADET. These links to reference materials from the explanation systems provide the basis for our navigational aid.

3. WebCADET as a Navigational Aid The problem of navigating through, finding, and accessing knowledge is one that is commonly observed in design. As described in Section 1.1, access and retrieval of relevant knowledge is a combination of standard archive mechanisms, and consulting with colleagues. There are a number of mechanisms already in place on the Internet, including: . keyword search engines, such as AltaVista; . catalogs, such as Yahoo!. Keywords and catalogs have advantages, but they both also create problems. For example, the keyword search engines often return a large number of inappropriate pointers; whereas the catalog, or taxonomic approach, requires a great deal of time in hand-assembling, and this often means that they do not link to new material for some time (as, for example, in DesignWeb). Moreover, although both automatic and manual refinement of keyword searches exist, such as WebCompass where users can define sub-categories of their search, they also return a large number of inappropriate ‘hits’. Table 2 illustrates the large number of returns and low number of ‘relevant’ hits using a selection of WWW search mechanisms currently available, carried out over a two hour period, on a specific search for ‘handle design’, undertaken by the authors. This shows that traditional WWW search engines merely serve to compound the problem of effective knowledge access and retrieval. Further problems with the search engine approach include the spasmodic availability of remote Table 2. ‘Handle design’ results by search engines Search engine

Hits

Hits viewed

‘Relevant’ hits

Alta Vista HOTBOT Yahoo! Lycos

970 656 227 138

200 200 100 70

10 6 5 1

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servers, out of date and repetitive knowledge, the possibility of overlooking an important document and the time it takes to go through the results. The main problem is the lack of content-awareness in traditional hypertext documents. Search engines must inspect all of the content on every page, and consequently return vast numbers of inappropriate hits. The imminent advent of XML (Extensible Markup Language) on the mainstream Web will provide a solution to this problem (Mace et al. 1998). XML describes the content of a document rather than the presentation (as is currently the case with HTML). Standard sets of XML tags have already appeared for a number of applications and others will follow suit. The greater accessibility and availability of meta-data describing the content of hypertext documents will substantially improve the relevancy of search engine results. Due to the time involved in checking through hundreds of document hits, only a percentage of the search engine hits were actually explored. The number of ‘relevant’ hits from the initial search were found from the percentage of hits explored. Using this approach, there is no guarantee that all ‘relevant’ hits will be viewed. Another method of navigational support, in addition to taxonomies and word searches is ‘social navigation’. This relies on the knowledge and contribution of other users on the Internet. There is a clear parallel between such ‘social navigation’, and the role of colleagues reported earlier in the study of designers in the telecommunications company (Rodgers 1997).

3.1. Social Navigation ‘Social navigation’ (Dieberger 1997) is based on communication and interaction with other users. A number of other ‘social’ mechanisms exist to support the WWW user in locating knowledge, such as questions posted to appropriate news-groups or to specific persons identified from their Web page interests. More recently, work on ‘shared bookmarks’ in which a user can explore sites book-marked by persons of similar interest has also been developed. Before the development of the search engines and catalogs, ‘social navigation’ was the only method of navigation on the WWW. The latter are often expressed as WWW sites where individuals have created links to other online resources which they believe to be interesting and useful. These are not formally constructed in the manner of a systematic catalog, but based on the experience of individuals,


who have located material relevant to their current project or profession, and which may be of interest to other users. WebCADET will offer a similar functionality to these ‘social navigation’ pages, by linking a number of similar pages together via taskspecific indices. ‘Shared bookmarks’ are usually a very general set of links to related materials. There is no real structure other than simple clustering under headings to aid other users of these pointers. Access to material would be made more effective if the pointers to online materials could be made appropriate to the specific problem at hand. The next section will describe how WebCADET will support such taskdependent access by providing structured pointers through the design problem space.

3.2. Task-Based Navigation WebCADET will also allow for authors to create task-based navigation links. That is, pointers to references from the rule-bases, or more usefully any explanation generated by WebCADET, create a ‘conceptual trail’ in the WWW. The stand-alone version of CADET merely listed the references in the conventional printed publishing format (Fig. 6), but WebCADET extends this principle. For example, if the reference is on-line, and it is expected that this will increasingly be the case, then the designer can hyperlink from the rule or explanation to the complete associated reference material. From this new WWW resource, it is then further likely that the designer will find links to related, additional, material. Unlike the ‘social navigation’ pages described above, however, the ‘trail’ is derived from decisions made during the design process. Also, the list of pointers to references is ‘dynamic’ and ‘situated’. The ‘trail’ is generated by WebCADET from the rule-base as the designer interacts with it. As these rules are amended and updated, this set of links is similarly revised. Furthermore, as WebCADET takes in input data from the designer, these pointers are ‘situated’, more appropriate to the current design task. Rules in the KBS are only triggered when the conditions match, and the pointers to support and associated materials when they are mostly likely to be of interest – in the appropriate situation within the design process. The notion of ‘situated-ness’ is one that is very important in managing the knowledge given to the designer. For any situation, certain knowledge is more appropriate than others. A KBS, such as WebCADET,


is an effective way of describing the current focus of interest of the designer, and thus co-ordinating the links to various on-line design knowledge resources. The idea of such ‘situated’, dynamic texts has been employed before by Simon and Duell (1992), who implemented ExpertBook. This is an intelligent manual for a power station, which displays those parts of the manual which were deduced by the expert system, from input data, to be of most interest to the power plant operating staff. The system gives the operators fast and situation-specific access to operating procedures. The structure of the CADET referencing system can now be seen as an extension of other structuring mechanisms in print, such as checklists, tables of content, indices, etc. The various WWW resources can now be linked by an additional layer of structure derived by their connection through the formal, inferential system (Fig. 8). For example, using a selection of the design tasks in the electric toothbrush example described in Cross (1994), Fig. 8 illustrates how designers will be able to select a number of KBS rules and supporting design knowledge on the Internet (via links from the explanation system) reflecting various design tasks, involving issues such as ‘safety’, ‘reliability’ and ‘cost’ as they explore the design space. Exploration of the design space is an iterative activity where designers consider many solutions and parts of the problem concurrently. WebCADET links to relevant knowledge and supplementary design resources will

Fig. 8. Task-based links from WebCADET via KBS ‘texts’ to WWW resources.

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be available for the designer to browse and utilize which will help cut down drastically the amount of time designers spend following ‘fruitless searches’. The next section of the paper describes the process of converting the original stand-alone version of CADET into WebCADET. This section also illustrates how WebCADET can be used to support designers during the early stages of design.

4. WebCADET: Adapting CADET to the World Wide Web The solution to providing distributed design support has been to extend the original stand-alone CADET system into WebCADET. It is deployed on a Web server enabling access via the Internet. This section describes the technologies employed in the development of WebCADET and also illustrates the use of the system through a typical product design example. WebCADET adopts the ‘knowledge server’ paradigm. This is one technique by which knowledgebased systems can utilize the connectivity provided by the Internet to increase the size of the user base whilst minimizing distribution and maintenance overheads (Eriksson 1996). WebCADET exploits the modularity of knowledge-based systems, in that the inference engine and knowledge base(s) are located on a server computer and the user interface is exported on demand to client computers via the Internet.

4.1. WebCADET Design and Implementation The development of the WebCADET prototype system has been a two-stage process. The first stage was to convert CADET into a stand-alone Win-Prolog application, involving the translation of the original knowledge base into an appropriate format and reconstructing the necessary functionality of Flex (as used in the CADET stand-alone version) into ordinary Prolog. The ProWeb Server toolkit (from Logic Programming Associates Ltd.) allowed the original CADET implemented in Prolog to be converted into CGI executables which were then deployed on a standard Web server. (CGI is an abbreviation for Common Gateway Interface and is the protocol which interfaces Web servers with external applications to generate dynamic knowledge in real-time). The second stage was to implement the WebCADET user interface in terms of template Web pages to contain dynamically generated input forms, the necessary code to extract knowledge from submitted forms, and display results.

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The internal knowledge representation used in WebCADET has a more concise format than that of the original CADET but is equally expressive. Each rule comprises four slots: . . . .

name; precondition; conditions; scale.

The name slot identifies the rule as being germane to a specific product attribute, with the precondition slot restricting the applicability of the rule to a specific product type. This allows the re-use of product attribute names such as looks_attractive for different products. The conditions slot contains the design knowledge which determines whether the values of design parameters inputted by the user (designer) increase or decrease the ability of the proposed design to meet the desired product attribute. These criteria are formalized as a set of if-then-else expressions with different scores being concluded in the then and else sub-slots depending on the validity of the criterion in the if sub-slot. The scores for each criterion are summed to a value between 0 and 1 using the scaling factor in the scale slot. As an illustration of the formalism, consider the design rule, reduce_decay, shown below: name reduce_decay precondition product_type electric_toothbrush conditions [ if filament_length gt 9 and filament_length lt 13 then 0.21 else 0, if filament_diameter gt 0.15 and filament_diameter lt 0.31 then 0.21 else 0, if filament_material has_aspect reduce_decay_ material then 0.16 else 0, if handle_shape eq contoured then 0.16 else 0, if tuft_arrangement eq tuft_arrangement_shape then 0.10 else 0, if angle gt -6 and angle lt 16 then 0.16 else 0 ] scale 1. The ability to reduce_decay is obviously a desirable attribute of an electric toothbrush. In this example, there are six relevant design parameters contained in the conditions list. Appropriate bounds on the viable values for each parameter are stated as Boolean expressions with gt, eq and lt abbreviating the ordinary relational operators of 4, =, 5, respectively. The has_aspect operator links a design parameter to physical properties of materials. Else-

where in the knowledge base the necessary physical properties (e.g. water absorption, material type, tensile strength), which a material must possess in order to be an appropriate choice to reduce tooth decay, are encoded as a rule. A further segment of the knowledge base contains factual knowledge relating to material properties. Whilst failure to meet a design criterion yields the partial score of zero, achieving a design criterion provides a variable result (between 0 and 1) related to the importance of the parameter in attaining the overall design attribute. The individual weightings of the parameters can be determined using use-value analysis techniques (Pahl and Beitz 1996) or by pair-wise comparison (Cross 1994). Justification of the design rules is facilitated by an ‘explanation fact’ for each condition in the conditions slot of design rules. The explanation facts are indexed by the conjunction of product type, attribute, and design parameter. For example, the length of the filaments should be between 10 to 12 mm is the explanation associated with the combination of electric toothbrush, reduce decay, and filament length respectively. Explanation facts contain canned text for both positive and negative outcomes of the testing of a design criterion as well as a list of citations. The citations are mapped to the corresponding full journal or on-line locations in a similar fashion to a traditional bibliography. For example, as mentioned previously in Section 2.2.1, the reference source Terry (1991) was used as rationale for the spacing of blades in a ‘wet shaver’. To improve the performance of WebCADET, two additional sets of supporting factual knowledge are links from: . product types to their respective design attributes; . product-attribute pairs to their respective lists of design parameters. The first set of facts allows the design attributes for a given product to be grouped into categories of need (e.g. physiological_needs, technical_needs, psychological_needs etc. in the example of the electric toothbrush). The performance increase attained here is by the elimination of the requirement to search the design rules to determine which attributes and design parameters are applicable in any given consultation. This is more time-consuming than a simple lookup operation. In the context of knowledge servers, giving the user a faster response is more important than using up a few extra kilobytes of disc space on the server. The inference process in WebCADET is fairly straightforward. Once the product type and design attribute have been established, and the design parameter data inputted by the user (designer), the


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inference engine proper is activated. The appropriate rule is matched via the contents of the name and precondition slots. The contents of the conditions slot is then extracted and the inference engine iterates through the list of conditions. The outcome of each condition both in terms of Pass/Fail results and the associated score is recorded to form a trace of the evaluation. Where necessary, rules relating to domain knowledge (e.g. properties of materials) are fired to establish the truth or falsehood of a design criterion.

4.2. WebCADET Example The current prototype implementation of WebCADET has been deployed on the World Wide Web* and may be accessed using any forms-capable browser. The operation of WebCADET can be considered at both the system and application levels. At the system level, a WebCADET consultation will proceed until a point is reached where it is necessary to acquire input from or send output to the user. At this point, the text to display and/or the forms to acquire data will be packaged into HTML (HyperText Mark-up Language) format and inserted into a template hypertext page. The resultant Web page is sent from the Web server to the remote user’s Web browser. On receipt of the Web page, the user completes and submits the form or clicks on some other interface component such as a hyperlink or a button. The user’s action is returned to the Web server via the normal hypertext transfer protocol and then communicated to WebCADET via the ProWeb CGI mechanism. WebCADET determines the nature of the user’s input or request and processes it accordingly. Processing continues until the next juncture for human-computer interaction occurs. At the application level, what is important is the process of design evaluation from product type selection to resulting score and associated explanation. A WebCADET consultation session, based on the aforementioned design example from Cross (1994), is illustrated in the following figures (Figs 9–13). First, the user logs on to the WebCADET system by entering the URL in a forms-capable browser. The WebCADET home page is downloaded and displayed in the Web browser. The user (designer) then completes and submits the log-in form. WebCADET responds by sending the user a page containing the set of product types which can currently be evaluated The *http://hibernia.eng.cam.ac.uk/proweb/cadet.htm

Fig. 9. WebCADET product type selection.

user then selects one of these (e.g. electric_toothbrush) from the menu and submits this request (Fig. 9). WebCADET inspects its knowledge base to determine whether the corresponding attributes have been grouped into categories for this specific product type, and on finding this to be true, returns the user a page containing a selection of attribute categories. The user selects a particular category (e.g. physiological_needs) and submits this. On receipt of this request, WebCADET retrieves the set of design attributes in the physiological_needs category of the electric_toothbrush product type, and packages this into an appropriate Web page for transfer to the user. The user then chooses a specific attribute (e.g. reduce_decay) against which the proposed design is to be evaluated (Fig. 10). Indexing on the chosen product type and design attribute, WebCADET retrieves the set of design parameters whose values will be needed in order to evaluate the design relative to the chosen attribute. A query dialog page designed to elicit the desired data is generated dynamically and a page containing all the questions (augmented with menus of potential materials, colors, shapes, etc.) to ease data entry is sent to the user (Fig. 11). Once the user (designer) completes the page giving a description of their concept design, WebCADET can begin the evaluation of the design. The reduce_decay rule is duly matched and the six design parameter criteria are processed in the light of the user’s design data. On completion of the reasoning process, WebCADET returns the score

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Fig. 12. WebCADET evaluation with explanation. Fig. 10. WebCADET product attribute selection.

Fig. 11. WebCADET concept design description form.

obtained for this design with respect to this attribute and indicates in the results page the Pass/Fail status of each design parameter (Fig. 12). To request an explanation of the evaluation, the user clicks on the ‘Explain the Result’ button on the results page shown above in the left hand window. The right-hand window in Fig. 12 illustrates the explanation which consists of the rationale for the score in terms of justifications and references, both to the design literature and various on-line resources (Fig. 13). The left-hand window of Fig. 13 lists the references used in the explanation shown in Fig. 12. The righthand window shows an example (Reference 6) of how on-line expert design knowledge and resources can be accessed during the design process. The search for relevant design knowledge to support the decision making activities commonly found in tasks such as

Fig. 13. WebCADET justification for evaluation result with associated on-line resource.

concept generation, concept evaluation and so on can be traced using WebCADET. These task-based traces, which contain links to relevant knowledge and on-line resources, will be effective in locating knowledge in similar future design projects.

4.3. Future Development In terms of future development, it is intended to extend WebCADET’s functionality in two major areas, through providing a knowledge base assistant and inter-server communications facilities. Evolutionary maintenance to extend and revise knowledge bases to include new knowledge and remove obsolescent knowledge is a form of software maintenance not found in conventional software systems (Watson et al. 1992). This is less troublesome in WebCADET owing to the autonomy of rules and


rule-sets than it is, for example, in diagnostic expert systems where chains of rules are essential to determine a probable cause for a set of symptoms. In such systems, adding new knowledge almost always involves knowledge base verification to ensure that the new knowledge does not contradict or interfere with established heuristics. In WebCADET, however, rules are completely separated from each other and so the effort in extending the knowledge base is confined to adding new rules and different versions of rules (see Fig. 7) and ensuring that such rules are appropriately linked to attribute categories and products, and that the necessary explanation facts are included. To ease the addition of new knowledge, and to allow designers other than the authors to expand WebCADET servers, an appropriate knowledge base assistant will be designed and implemented. One of the aims is to have multiple but distinct instances of WebCADET deployed at various sites. Each specific WebCADET server would then grow to encompass the design knowledge of the designers at each site. By providing WebCADET servers with the capability to communicate with each other, the intention is to harness this future community of design resources so that when a local server is unable to evaluate a given design, it will be able to automatically interrogate other servers to utilize their knowledge. As rules will be marked with both their applicable context (e.g. what constitutes an attractive color for a particular product will vary by culture and region) and their author or source, quality control in the WebCADET community will be in terms of actual usage of the design rules (‘texts’) with servers containing high-quality knowledge thriving and less well-respected servers falling into disuse. Thus, it is envisaged that a market in knowledge base versions will emerge.

5. Summary The efficiency and effectiveness of design knowledge retrieval systems cannot be measured solely in terms of their search times in use. The ability to determine relevant knowledge as well as providing a ‘browsable’ and ‘searchable’ structure are believed to be far more significant and of far greater value in operation. WebCADET meets these requirements by providing a highly practical means of finding relevant design knowledge efficiently and effectively. By understanding KBS resources as an extension of ‘text’, it is a simple step to interpret the structuring mechanism of WebCADET as a dynamic and

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structured ‘text’, and one that can also act as an index to support material that is present on the WWW. In this mode it can be seen as a task-based navigation aid to the WWW-based content. As authors make their rule-bases and supporting multimedia material available on the WWW, be it on an intranet, extranet or the Internet proper, it is hoped that a library of expertise will grow. Such dynamic libraries can aid the distributed and flexible project design and development teams that are currently emerging.

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