does intelligent cad exist?

Does Intelligent CAD exist?

K . J . M a e C a l l u m

CAD Centre, University of Strathclyde, 131 Rottenrow, Glasgow G40NG, Scotland

Intelligent CAD is a term which has grown in popularity in recent years as the results of research into Artificial Intelligence (AI) get applied to design problems. But there is confusion as to what the term means; e.g. whether current generations of expert systems for design are Intelligent CAD, or whether Intelligent CAD is simply an unachievable goal.

This paper argues that Intelligent CAD represents a vision which is almost identical to the earliest visions of Computer Aided Design (CAD). The major difference now is that we have the opportunity through our improved knowledge of AI and cognitive science to take important strides towards delivering CAD systems close to these visions. However, the opportunity may be missed if we concentrate on the wrong issues.

It is also argued that developments towards Intelligent CAD will be successful only if they accept the following tenets:

(i) design is considered as an intellectual knowledge-based process, (ii) systems do not need to replicate human intelligence; they are required only to exhibit

behaviour regarded as intelligent, and (iii) necessary components of such systems are knowledge-rich models of designs, the capacity for

tacit knowledge, and the ability to learn.

Key Words: design, CAD, intelligence, knowledge, dialogue, learning.

1. INTRODUCTION

Within the last decade considerable interest has been expressed, and attention given to the development of computer-based design systems which use techniques evolving from the field of Artificial Intelligence (AI). In particular we have seen the rapid emergence of specific expert design systems, and expert system shells being applied to design tasks such as configuration, advice giving, data access and decision support. A natural consequence of this effort has been the concept of 'Intelligent CAD' (referred to as I-CAD in this paper); that is a computer based design system which has intelligence. In support of this concept, various research groups have been formed, research programmes established, and workshops and conferences run. Despite the high level of activity, there seems to be a diversity of opinion and a great deal of confusion as to what we mean when we talk of I-CAD, and indeed, whether it even exists. There are those who can point to working systems and claim they have achieved I-CAD; there are those who believe an intelligent system is a constantly receding goal and can never be achieved. What, therefore, is I-CAD, and does it really exist?

From those working in the field of I-CAD the best descriptions of I-CAD are expressed as visions. The visions are conveyed in metaphors like 'Designer's Apprentice' or 'Expert Designer'. Thus, MacCallum presented the idea of an Intelligent Design Assistant (IDA) 1, i,e. a system which takes an active role in the design process and which is able to contribute to the work of the designer. The IDA understands goals, proposes solutions, advises on methods, and assesses situations; in

other words it takes a broader and more responsible role in design than conventional systems. The implication is that there is a major shift in the balance between the designer's and the system's roles. The system comes closer to being a partner in a cooperative process. Forbus 2, in a discussion on Intelligent Computer Aided Engineering (ICAE) echoes this idea; he states that the goal of ICAE is to construct computer programs that capture and use a significant fraction of the knowledge, both formal and tacit, of engineers. He then presents a number of scenarios to illustrate the concept. In one of these concerned with design, he visualizes that the system:

- offers basic concepts (presumably understanding objectives),

- warns against unsuitable alternatives, - suggests possible solutions, - reminds, - qualitatively simulates, - diagnoses and refines.

The two visions are essentially similar and probably represent commonly understood, if unexpressed, views of I-CAD.

But how is this vision to be achieved? It is now widely accepted that perhaps the most promising approach is to realize some of the potential being offered from the field of Artificial Intelligence (AI). Forbus tells us that AI is a particularly good technology for engineering 2. He justifies this by referring to the increasing complexity of projects and costs, the new materials and processes which are available, and the stricter regulations and legal

© 1990 Computational Mechanics Publications Artificial Intelligence in Engineering, 1990, Vol. 5, No. 2 55

Does intelligent CAD exist?: K. J. MacCallum

requirements which need to be satisfied. He suggests that AI will provide the much needed help.

What is disturbing however, is the extent to which the terminology of AI has become abused and devalued in its application. It derives perhaps from the very difficulty of defining terms such as knowledge, meaning, explanation or intelligence, or perhaps from the attraction of achieving human and biological mimicry. In any case, in practitioners claiming to be achieving Intelligent CAD, we can observe a healthy disregard for the deeper significance of the terms they are using. One of the most stunning examples of this appears in recent generations of CAD/CAM systems. In a number of these, a valuable feature is the ability to attach an attribute to a geometrical grouping or item. Thus a user of a system could decide to attach an identification code number, a colour value, a material description or whatever. In some cases these attributes are called intelligence!! It seems the marketing and sales staff believe this is intelligence. Is it possible that the system developers and the users also believe this? Closer to our own field of I-CAD it is not too difficult to find examples of researchers who consider that their systems are intelligent. At a recent workshop where a question regarding intelligence was asked, the response was quite unequivocal - the system has intelligence because it is written in Prolog! Chandrasekaran has a similar concern when he says that 'We started from AI, which then became expert systems, which then became rules, which then became LISP. So people think they are doing AI when they do LISP .. ?3. We should avoid the superficial systems which pretend to be intelligent. I do not argue that such systems are necessarily bad; only that we should be clear of the part they play in our work - they are not by themselves I-CAD.

It seems that I-CAD exists as a vision but not yet as an artifact. There would appear to be a gap between what is promised by AI, and what is currently being delivered in CAD systems. Does this mean that I-CAD vision is unachievable? Or is the application of AI the wrong approach. Perhaps the current generation of systems and products do indeed represent I-CAD?

2. AIMS OF THE PAPER

The issues which are raised by the above questions are more substantial than the questions themselves would suggest. Of primary importance is the relevance or otherwise of AI to the design community. The usual view is that applying AI to CAD is a way to make further progress in design support. However, if such an approach is not able to demonstrate its advantages, potential or real, in a very substantial way, research in the field will be seriously undermined. It is in fact the 'AI Winter' argument in the context of CAD objectives. It is essential therefore that we establish a proper interpretation for the term I-CAD, which strikes a balance between the toy systems and the unachievable. Such an interpretation, if widely accepted, will act as a research goal justifying investment of effort, will provide a basis for measuring progress towards that goal, and will isolate research and development which although using AI techniques does not form part of the I-CAD goal.

The overall aim of this paper is to present a personal view of the interpretation which should be adopted for Intelligent CAD. The interpretation is based on four

parts:

(i) the rationale for bringing AI technology to bear on our CAD efforts

(ii) a view of design as a total and intellectual activity (iii) an understanding of the requirements for an

intelligent mechanism (iv) an identification of the minimum necessary com-

ponents to build an intelligent mechanism.

3. PROGRESS TOWARDS INTELLIGENT CAD

One of the earliest, and most significant, inputs to the discipline of CAD was made by the Sketchpad system 4. Sketchpad was based on work carried out in the early 60's, and showed the first examples of using a computer for interactive creation of geometry. The impact of Sketchpad was significant in the history of CAD, partly because it demonstrated a philosophy rather than just talking about it. What did Sketchpad achieve? Most obviously, it demonstrated the potential of computer graphics for interactive geometric modelling. Although it took many years for hardware to develop, for the software concepts to mature, and for industry to find the ideas acceptable, Sketchpad was the forerunner of our current generation of CAD/CAM draughting and modelling systems.

There was however another feature of Sketchpad which was largely overlooked in the early days - it was the concept of building and applying general problem-solving techniques: in this case, constraint-based solution techniques. In Sketchpad the technique used a least squares fit applied to geometric equations for under, over or uniquely constrained problems. It was this technique which allowed the application, for example, of horizontal/ vertical line drawing with closed figures. To get some feel for how this could be applied more widely, it is worth examining a paper by Mann and Coons 5. The paper published in 1965, reports on an exercise undertaken with Sketchpad which tackled the following five problems:

(i) Mathematics: evaluating a cubic polynomial whose coefficients and arguments are all complex numbers for various values of the argument.

(ii) Geometry: drawing a three-dimensional array of connected lines and passing a smooth curve through the vertices.

(iii) Kinematics: constructing a mechanical linkage, attaching points to the (extended) coupler bar of the linkage, rotating the driving link and observing the paths of the points.

(iv) Stress analysis: drawing a two-dimensional space frame, applying loading, and observing deflections of the members under load.

(v) Fluid mechanics: drawing an arbitrary boundary, and mapping the stream and equipotential curves for an ideal fluid flowing in a bounded region.

Although the problems are not very closely related, and a computer program written explicitly to solve one of them would not be of any use with the others, the constraint satisfaction mechanism of Sketchpad was general enough to solve the entire set of five problems at one sitting. It is this generality of application coupled to model building which underpins the CAD philosophy in Sketchpad.

56 Artificial Intelligence in Engineering, 1990, Vol 5, No 2

Progress

I

I

I

t I I 1960 1970 1980 1990

Fig. 1. CAD progress

I 2O0O

The philosophy or vision of CAD at this time was summed up in the same paper:

'It is clear that what is needed if the computer is to be of greater use in the creative process, is a more intimate and continuous interchange between man and machine. This interchange must be of such a nature that all forms of thought that are congenial to man, whether verbal, symbolic, numerical, or even graphical are also understood by the machine and are acted upon by the machine in ways that are appropriate to man's purpose.'

From the time of Sketchpad, we witnessed a very rapid development in CAD technology, enabled by developments in graphics hardware, time-sharing, geometric modelling, numerically based analysis techniques, and data bases. However, the rate of development has now levelled out. In the present decade, systems are faster, algorithms are more refined, applications are wider, usage is more robust, take-up is more extensive; but overall development has slowed. Essentially we have reached a plateau; CAD has stagnated well short of this first vision of CAD (Fig. 1).

However, the coincidence of the CAD vision with our current I-CAD vision is remarkable. It is this coincidence which leads us to think of a CAD system as some kind of an intelligent mechanism.

Sloman 6, gives a generalized sketch of an intelligent mechanism as something which must be able:

- to notice something it was not explicitly looking for - to interpret, abandon, modify, or suspend some or all

of its current action - to search through its stock of resources for items

which satisfy a current requirement or need, possibly in an unforeseen way

- to relate parts and effects of one action to purposes, preferences, or other motives besides those which generated the action, or more generally to relate facts to problems and purposes not currently being attended to.


It seems that the intelligent mechanism could satisfy our vision for Intelligent CAD.

Here we see the rationale for articulating the I-CAD vision, and for bringing AI technology to bear on CAD problems - n o t because it is intellectually stimulating; not because it is eye catching; nor, least of all, because it supports incremental improvements in existing system capabilities. But rather because it offers the potential for a step function improvement in the performance, behaviour or capabilities of our present generation of CAD systems and allows us to get back on a steep ramp of progress (Fig. 2). It is not just because I-CAD and AI contain the word 'intelligence' that they have something to offer one another. We could easily drop the word from both, or find alternative titles and still find that one will benefit from (and needs) the other. The AI topic offers a promise of delivering the vision of I-CAD as an 'intelligent mechanism', and in so doing to deliver the earliest visions of CAD.

4. TH E I N T E R P R E T A T I O N OF DESIGN

Before exploring the implications of an intelligent mechanism, it is necessary to remind ourselves of our understanding of design. If it is possible, there are as many definitions of design as there are of AI. It is not the aim here to add to this list but rather to focus attention on the viewpoint of design which seems to be relevant to the concept of Intelligent CAD. It is apparent from the literature on I-CAD, and indeed CAD, that design is often treated in a narrow or 'small' sense. For example design problems are concerned with a specific design task such as selection or analysis, or are concerned with domain- dependent knowledge. These approaches, while often more tractable, avoid issues of design in the 'large'.

Bijl 7 refers to the world of design as 'the thoughts in the heads of designers, plus the skills of designers in externalizing their thoughts'. We are reminded here of an essential truth about design - it is an intellectual process.

I t I I

~0 1970 mo 1990 2ooo

Fig. 2. The goal of I-CAD

Artificial Intelligence & Eng&eering, 1990, Vol. 5, No. 2 57

Does intelligent CAD exist?." K. J. MacCallum

DESIGN PROCESS

oo. n

t '~

background

problem ~ EXTENT = PRODUCT models ~.

/ % ' e ~ solution

% % % % °%%%%

-% • ~ o o "~ oj

o ~ o ~ ~ = ~

DEPTH = KNOWLEDGE

BREADTH = MECHANISMS

Fig. 3. The knowledge cube

The 'thoughts', 'skills', and 'externalization' are fundamental aspects of the design process. The viewpoint does not exclude analytical methods, engineering drawings, or design management and decision making from the design process - it does, however, relegate them to a secondary role in design, in favour of conceptual or creative activities. In the context of engineering design, it also focuses our attention on the cognitive, or knowledge- based aspects within the design activity. Various researchers have tried to categorize that knowledge so that it can be better described, formalized, and used. Forbus 2 identifies some of the knowledge and skills required by systems as:

Broad domain knowledge Layered domain knowledge Routine design (past design knowledge with incre-

mental procedures) Functional descriptions Qualitative simulation Procedure generation Failure analysis and fault-tree generation Communication skills.

a knowledge based activity. However, they tell only part of the story. Simon's 'Sciences of the Artificial '9, presents an important perspective for I-CAD. Simon contests that certain phenomena, including design, are 'artificial' in the very specific sense that they are as they are only by being moulded, by goal or purposes, to their environment. He contrasts the air of 'contingency' of artificial phenomena in their malleability by environment, with the air of 'necessity' of natural phenomena in their subservicence to natural law. He goes on to describe design as 'how things might be rather than how they are' - i.e. design is concerned with the contingent.

The interpretation of design which emerges from these arguments is that design is concerned with the contingent, that it is concerned with a deliberate activity to mould the environment, and that it is essentially an intellectual and knowledge-based activity. It is this generic, domain- independent, cognitive view only, which is of relevance to I-CAD.

5. REQUIREMENTS FOR AN INTELLIGENT MECHANISM

In Ref. 8, an independent taxonomy of knowledge in design was developed based on three dimensions: DEPTH of knowledge varying from facts and shallow level rules through to underlying commonsense knowledge, BREADTH of knowledge covering the variety of domains involved, and EXTENT of knowlege referring to the scope of the design process to be included. This approach resulted in the knowledge cube (Fig. 3), with design activities corresponding to a set of transformations from one part of the cube to another.

Such models have proved valuable in presenting design as

Just as it has been important to place the word design in its proper context, so it is necessary to examine our understanding of what we require for an intelligent mechanism. It is not the place of this paper to undertake a philosophical debate on the meaning of intelligence (or knowledge, meaning, explanation, learning). However, it is rewarding to listen to the viewpoint of AI workers. This viewpoint is typified by a statement from Barbara Hayes-Roth in a panel discussion which began 'I am interested in intelligence '1°. She went on to explain how this interest had driven her to investigate the behaviour of

58 Art(/icial Intelligence in Engineering, 1990, Vol 5, No 2

cooperating autonomous systems as examples of intelligent behaviour and that she was doing this in the context of a number of human problem solving situations. Design was just one of these. This view is also strongly established in the work of Smithers 11. Forbus in a mirror image of his view that AI is particularly good for engineering also says that engineering is a good source of problems for studying AI 2. These statements should not surprise us coming from workers whose main field of study is AI. Indeed it could be argued that we should adopt a similar approach in our search for I-CAD. Without studying intelligence how can we hope to deliver our goal of Intelligent CAD?

But for I-CAD the study of intelligence for its own sake is not appropriate. If we remember that our goal is purely a step function improvement in CAD then our concern should be how to improve performance and not what is the fundamental nature of intelligence.

With this more limited viewpoint, two aspects become crucial to our interpretation;

(i) the key role played by system external behaviour, and (ii) the importance of dialogue as part of that behaviour.

5.1. Behaviour It is Simon 9, in his Sciences of the Artificial, who argues

strongly for the significance of the 'behaviour' of systems. There are three parts of the argument which are of direct interest here. First, he says that a system can be thought of as the interface between an inner environment and an outer environment. Where the inner environment is invariant the function which the system performs depends only on the outer environment. This concept of functionality is valuable in design since it avoids the common but false assumption that function, or purpose, is an innate property of an artifact. Secondly, he argues that the behaviour of an artifact in an outer environment can be adequately described (simulated) without a complete working description of the artifact. Abstracting from the detail of a set of phenomena makes it easier to simulate the phenomena. In particular, we do not have to know or guess at all the internal structures of the system, but only at that part of it that is crucial to the abstraction. The third part of the argument is that in a complex system the organization of parts provides an essential component of simulating the behaviour of the system. Indeed, the concept of hierarchical organization is seen to be essential in handling complexity.

These arguments support the concept of modelling in CAD, in which abstract models of artifacts are made to exhibit behaviour similar to the actual behaviour of the artifact. However, our interest in this paper and the relevance of these arguments in on the design system as a model of an intelligent mechanism. The fact that the three parts of the argument apply to this case should be of some encouragement to us in seeking the goal of Intelligent CAD, since it demonstrates that we do not need to understand fully the mechanisms of intelligence in order to construct systems which exhibit 'intelligent' behaviour. It suggests that there are alternative structures or abstract models which could give the desired behaviour of a designing function when placed in appropriate environments.

We have argued that what we should be concerned with is capturing those intellectual processes associated with the designing activity, as opposed to developing purely analytical tasks within design. It is these intellectual processes which will form the basis for building systems which are able to contribute to design in an intelligent way.


Simon's arguments in addition, show that to achieve a design system exhibiting intelligent behaviour it is not necessary for the system to operate in a way identical to a human designer. However, it is important that the system is able to represent knowledge, to abstract and organize that knowledge in a way which enables complex behaviour to be simulated, to recognize the distinctions between artifacts, inner and outer environments, and to distinguish between the necessary, the contingent, and the desirable.

5.2. Dialogue The place of dialogue as an aspect of Intelligent CAD

derives from a perceived role-model for such a system in which there is an interaction between the system and the outside world. This may seem at odds with the alternative of 'automated' design which sometimes emerges when applying AI to CAD. In a search for better models of the design process Mostow offers 'mechanizing' design i.e. moving the design process into the machine, as a goal12. He simply relates the extent to which design can be automated with the enhancement of the productivity of human designers. While the role of automated design may not be inconsistent with an interaction with the external environment, there are a number of arguments which we should employ against the goal of a totally autonomous, automated design machine.

Designing is presently a human centred activity. Our goal is to replace this human centred activity with computer centred activity; that is a role replacement. If we are concemed with automation as the role replacement, then our first question should be: 'Does our subject of study (the designer) treat design as an automated activity?'. The answer is, 'No'. Designers operate within an environment and with a purpose which necessitates communication and interaction. At one level, designers have to interact with other designers; at another they interact with the customers and consumers of their work. Cooperation and management of resources are essential ingredients of design. More significantly, designers respond to world situations and produce results which are intended to impact on that world situation. These again involve interaction and communication in many different ways which are crucial to the success of designing. In order to design, any system must interact with its environment.

As a minimum, an automatic design system must accept some input and produce some output (Fig. 4a). But are we content that in every other respect the system is automatic? What if we omit some crucial aspect of the environment for which it is designing? Or suppose we incorrectly or incompletely express the problem? Or what if the results produced leave some doubt about their interpretation? Not only would such a system need to know a great deal about designing; but it would also need to know a great deal about

System

User Fig. 4a. Automated design system

Artificial Intelligence in Engineering, 1990, Vol. 5, No. 2 59


System

Fig. 4b.

User Interactive design system

the world it is designing for, and it would have to be able to interact with that world about its knowledge and its activities. How else can it update its knowledge and learn from its successes and failures? Even if we accept that design can be automated in the above sense, who or what takes the responsibility for the design? If we have a failure, do we sue the design program? Such arguments should emphasize that the acceptance of responsibility by a computer program is an unlikely event and that some person or organization of people would retain responsibilities for decisions. Are such people willing to accept the output from an automated design system without a full dialogue of the rationale and reasons for decisions

It seems clear that we should embrace an I-CAD concept which includes dialogue and interaction with the outside world (Fig. 4b). The danger of pursuing an automated design approach is not our emphasis on automation per se, but rather our lack of recognition of the need for open dialogue. To behave in an intelligent way, a system must be able to participate in an open dialogue with other intelligent systems. Unless we recognize this when designing systems we are likely to derive architectures and representations which are inappropriate to our goal.

Thus the role-model proposed for I-CAD is that of the design assistant which is an 'amplifier' of human intelligence. Where this argument has been made elsewhere, at least part of the foundation for it has been pragmatic. That is, because we have little chance at present of understanding enough about the intelligent processes in design to build an automated system, we should at least push the boundaries of what we do know with interactive systems. Having had a number of years to think about this and practice it, I now believe that the argument is false. The dialogue is not just convenient, it is crucial to success. It is worth noting in passing that the demands for communication place a much heavier burden on the 'intelligence' of the system than the automated approach.

In summary, therefore, the role-model for I-CAD is that of a design assistant: a system which is able to take a pro-active part in designing and which is able to bring to bear its particular strengths in design problem solving. The system is closer to a complementary relationship with the designer, but relies on the designer to identify and present the problem, to specify the environment, and to assume responsibility for decisions.

6. BUILDING THE INTELLIGENT MECHANISM

Up to this point we have concentrated on the external aspects of our Intelligent CAD concept and presented

them as requirements for an intelligent mechanism. It is now appropriate to look at the internal aspects and determine some of the major components of an intelligent mechanism which will deliver the type of behaviour suited to interactive design.

6.1. Models of designs The first of these components is the model of the artifact

within the system. With our long history of CAD, and with early successes firmly based on the ideas of representation of models, we may be forgiven for taking for granted the existence of a model of the design in a system. Yet, much of the recent work on building knowledge and expertise into design systems seems to have forgotten this heritage.

Without doubt, the major advance which has been instrumental in bringing an I-CAD community into being has been that of 'Expert Systems'. The initial foray into expert systems concentrated on problems concerned with diagnosis, recognition, and decision support. However, the promise of usefully capturing all aspects of human expertise and experience meant that it was adopted rapidly by problem solvers in areas such as planning, synthesis and. of course, design.

There are many published examples of reasonably sophisticated systems tackling real design problems. They are organized to contain significant amounts of design knowledge and they structure this knowledge according to its function. For example, in one system knowledge is organized into:

domain knowledge -a hierarchy of subsystems and components

constraint knowledge ~lomain specific knowledge procedural knowledge-relating to design procedures analysis algorithms -using quantitative evaluation solution knowledge -e.g. a block diagram schematic

In general they tackle the intellectual view of CAD, they are interactive and they exhibit interesting, if not quite intelligent behaviour. Yet they fail to match our vision of I-CAD. Why should this be?

A major failing is that they are unable to undertake an open interactive dialogue about the thing they are designing. Although they may be interacting with the designer to obtain information and to present results, and although the expertise in the system is separated from any preconceived ideas on its sequence of use, yet the overall organization and strategy adopted by the system in solving problems is pre-defined. While it is tempting to argue that strategies can be varied, in practice there is an absence of the information and knowledge which is vital; that is, the object of the dialogue - the DESIGN.

How is it possible to build a design system which does not know about a design? It is possible by organizing the design process as a series of inter-related decisions about a set of variables or parameters of the design model. The variables or parameters may be entirely independent in the system representation although they are strongly dependent within the artifact. The interdependencies are expressed through and contained in the compiled design rules, or through the interpretation of the results. Here we begin to see what we have inherited from the expert systems methodology. My contention is that it is not feasible to have an intelligent dialogue about a design unless that design is properly represented somewhere. The

60 Art~'cial Intelligence in Engineering, 1990, Vol 5, No 2

conclusion from this is that, by itself a production rule based approach as used in many expert systems will fail to give us Intelligent CAD, no matter how much expertise and knowledge we include in the system.

To re-examine this contention in the light of our aims for Intelligent CAD, it is valuable to see what we can learn from studies of intelligent systems. Johnson-Laird presents a cognitive scientist's approach to the way people think, and how our mental concepts of the world relate to the world itself and to the language which we use for its description la. It is written with computer processing in mind, but not addressed directly to those involved in building computer systems. However if we recognize that the cognitive process is the model for the intelligence in our I-CAD system then Johnson-Laird's work offers some important messages for our work.

Mental models have a direct representational equiv- alent to the thing which they model. They provide effectively working models of particular real world phenomena. The mental model is one of four different representations which Johnson-Laird refers to as composing the contents of the mind. The other three are propositional representing statements of situations, images, representing views of mental models, and procedural, representing methods of doing things. Central to these is the mental model. This is claimed to be necessary to understanding. As an example, consider the following description of the layout of the ground floor of a house and then answer the following two questions.

The house was square sitting in a square garden. As we entered the front door into a square hall, the lounge was on our immediate left, and the dining room to the right. The kitchen was at the back of the house and had a serving hatch to the dining room. From the kitchen, I could go directly to the utility room, and then to a toilet in the back corner of the house. Questions: Q.1. What rooms are next to the downstairs toilet? Q.2. What rooms are at the back of the house?

Figure 5 at the end of the paper illustrates a possible arrangement. Almost certainly, you will have managed to answer the questions correctly without looking at the figure. But how did you do it? Johnson-Laird's contention is that it is achieved by constructing incrementally a mental picture, or model, of the layout. This is the result of a transformation of the description, but allows the derivation or inferencing of additional information which is not directly available from the description.

A principal argument from Johnson-Laird is that to understand a phenomenon is to have a working model of it. This makes a striking contrast to the 'expert systems' approach to I-CAD.

If we look at some other AI based CAD systems, we can see that this message is beginning to be appreciated - models of design artifacts are being included. For example, DESTINY ~4, a system for integrated structural design, defines an abstraction hierarchy of objects within a blackboard architecture. The objects have class and part structure with relationship classes including generaliz- ation, classification, aggregation, and alternation. The blackboard is operated on by knowledge modules which consist of strategy, specialist, and resource knowledge


modules. We see in this sytem an increased emphasis on the design model; but the model structures are sterile without the existence of the knowledge modules. In other words, it is not feasible for a design model to exist as an intelligent model without at least some of the deeper 'resource' modules. The EDISON system x5 is an engineering design invention system operating naively. The approach is to have a system capable of creating novel mechanical devices by using knowledge of naive physical relationships, qualitative reasoning, planning and discovery of heuristics applied to abstract devices. The system addresses a number of issues including:

- how devices are represented and manipulated - how devices are organized, indexed etc. - how new devices are discovered

- how resulting inventions are accessed.

In this system we see most strongly the switch from an expertise driven approach to a model driven approach, together with a recognition that the model knowledge includes basic mechanisms for interpreting model structure. This same model driven approach is behind the concepts of knowledge chunking presented by Gero and Maher xr. Here the basic concept is that of a design prototype. Design prototypes contain the structural information of previous designs together with the resource modules which 'contain the common knowledge which supports more than one design prototype'. In all the above examples the design process is oriented round the design model and is not an abstract set of heuristics.

This analysis and viewpoint is by its very nature somewhat subjective. Certainly, the expertise based systems have demonstrated more exciting results more rapidly. Regardless of the above arguments they have also served a valuable role of investigating, organising, formalizing and reasoning with design knowledge. The argument here is that, by themselves, they do not provide a sound basis for Intelligent CAD.

6.2. Know&dge The second major influence on the capability of an

I-CAD system is knowledge. We have already referred to the importance of knowledge in our discussions on design, and recognized in the previous Section the relevance of an organization and representation for that knowledge. However, a feature of this which is only now becoming recognized is the concept of knowledge 'depth'. Forbus identifies the narrowness, uncertain coverage and brittleness of knowledge in a rule-based approach 2. He argues for problem-independent representations and domain-independent theories of reasoning. Here and elsewhere we see justifications for paying attention to the general, or deep, knowledge which should underpin the heuristic reasoning of rule-based systems.

Again, we can look to the work of Johnson-Laird to guide us as to what is necessary for I-CAD. The second valuable concept which is found in Johnson's work is that mental models are constructed by employing tacit mental processes. This concept is argued from the theories of language, explanation, and understanding. Mental inferencing is divided into explicit and implicit inferences. Implicit inferences are those which occur tacitly; they are not consciously followed through as a series of steps, they -occur almost as a matter of commonsense. The role of implicit inferencing is evident in the process of



communication. He argues that without an ability to make implicit inferences, written and spoken discourse would be beyond anyone's competence. Johnson-Laird contends that the mental mechanism is likely to consist of a device that constructs a single mental model on the basis of the discourse, its context and background knowledge. The background knowledge is in the model by default, that is, it is maintained in the model provided that there is no subsequent evidence to overrule it. The message for Intelligent CAD should be clear. In a situation where intelligent discourse is to take place, there must be processes of implicit inferencing based on tacit knowledge.

While this argument may apparently contradict the valuable trend to make knowledge in a system explicit, it is in fact quite consistent with that trend. The argument for tacit knowledge is not that a certain body of knowledge should be hidden and inaccessible; but rather that it should be feasible to carry on a dialogue without making every piece of knowledge needed for the dialogue explicit. Thus we would expect an Intelligent CAD system to have available, as a resource, a body of knowledge needed to carry out this role.

In this argument there need be no pre-conceptions about how tacit knowledge is to be represented. Indeed, I believe that the need for tacit knowledge as a basis for intelligent dialogue supports the current research into qualitative reasoning and other types of 'deep' knowledge representation.

6.3. Learning The third and last major influence making up the

structure of our I-CAD system is the ability of the system to learn.

Learning can be defined as any change in a system which allows it to perform better on the repetition of the same task 17. It seems self-evident that in human systems the ability to improve performance is fundamental to the display of intelligent behaviour. There are a number of ways in which this process appears, the greatest of which is the very rapid accumulation of knowledge and skills in children. However, even the daily occurrence of adapting behaviour to suit the people we meet and interact with is evidence of our learning.

Interaction with an outside environment is a necessary aspect of learning. It is this interaction which both stimulates a need to change behaviour and provides the evidence of the relevant features of the environment's behaviour. However the result of learning is a modification of the internal structure or information content of the system. The modification is based on the system's own perception of its goals and on a recognition of what in its organization should be modified and what should be kept the same (the invariants in Simon's work). In many areas learning is somewhat formalized into a recording in some medium of known facts, information and knowledge which can then be communicated to others. Seldom is such recorded information totally static; much is subject to regular revision according to new observations, experiences, or theories.

In the world of design it is possible to identify a number of categories of information and processes which are the results of design learning and are subject to revision and change both across different disciplines of design and across different design experiences in the same discipline.

These can be summarized as:

Experience of past problems and their solutions. This forms one of the most powerful and important sources of solutions to design problems. Whether the design being undertaken is routine involving a simple variation of a previous design, or whether it requires a conceptual leap using an analogy, past designs are the major building block of design experience. The learning from this is not a simple matter of recording previous work however. A quality assessment has to be made of the results of that work (there are more bad experiences than good ones), and the results of that assessment together with the solution has to be organized into a form which will be suitable for later use. This may involve abstraction, classification, and transformation.

Skills in conducting design. The whole process of undertaking design, managing the available resources, developing strategies, resolv- ing conflicts etc., depends for its success on good design experience. In design protocol studies it has been shown many times that experienced designers in all problem areas can reach satisfactory design solutions more effectively than naive designers. This type of conclusion is independent from any considerations of the degree of innovation or novelty apparent in solutions.

Categorization and use of concepts. The ability to deal with relevant information at various levels of abstraction together with a dynamic organization of that information into intersecting classes is a powerful tool in all kinds of problem solving. An important feature of this capability in humans is the ready ability to re-organize structures, to find links, similarities and parallels between disparate bodies of information, and to extrapolate existing concepts into new situations.

New technologies and practices. Since design is largely dominated by the technologies available for the construction of artifacts, a constant updating of the knowledge of these technologies coupled with the development of the practices required to use them is essential to the creation of competitive design solutions. While some of this learning is associated with formal gathering of knowledge available elsewhere, at least part of it is concerned with the appreciation of new opportunities and the consequent innovative use of the technology.

To begin to move towards our goal of Intelligent CAD according to the vision presented earlier, it is clear that we must endow our computer based systems with the same kinds of learning capabilities as real designers. We might reasonably modify this ambitious objective in recognition that our computer based system is unlikely to have the capabilities of humans to explore and observe the total environment in which the design artifacts will reside. Nonetheless, for the system to become close to being a partner in the design process, we would expect a high level of relevant work knowledge together with the mechanisms for continuously and readily updating this knowledge.

62 Artificial Intelligence in Engineering, 1990, Vol 5, No 2

Important learning mechanisms are by rote, by example, by analogy, and by exploration. These have different degrees of complexity and difficulty, but significant advances have been made in the field of AI in at least some of them. It is important to recognize that while all systems can have their performance improved by an external agent, the key achievement here is to have the system learning independently and autonomously 18. Thus although all mechanisms imply the existence of outside agents, they also imply that the system has a wider variety of sensors to the outside world.

The problems of systems which can learn is challenging, but fundamental to the I-CAD vision.

7. DISCUSSION

In this paper, we have posed the question, 'Does Intelligent CAD Exist?' Although the question could be dismissed as being trivial, I have tried to show that it is in fact the source of a number of serious issues and points of debate. The paper has argued that Intelligent CAD exists as a vision which represents a goal of having systems which play an active rather than a passive role in the design process, and which achieves that goal by incorporating significant amounts of design knowledge in the system. Despite the considerable research efforts in the field, however, the goal is not yet achieved. In particular, the tendency to 'oversell" recent developments in computer aided design systems as being intelligence only serve to devalue the major research effort taking place.

However, the paper has suggested that I-CAD is achievable. The goal of Intelligent CAD has been placed into a context of what can be learnt from the field of AI, and has provided an interpretation which allows us both to set some criteria and to understand the outstanding issues.

The criteria and issues are based on the following arguments:

(i) We refer to 'design' as the intellectual process of determining the contingent. We are not dealing with narrower aspects of design analysis, design modelling, or parametric/variational design.

(ii) We are interested only in systems displaying task behaviour which is normally regarded as requiring intelligence. We are not concerned directly with understanding mechanisms of human intelligence; nor do we need to know these mechanisms. The prime responsibility is to find suitable computational models of intelligent behaviour.

(iii) An essential requirement on the nature of that behaviour is the ability to carry out an open dialogue with external and independent intelligent agents.

(iv) To achieve intelligent behaviour in the domain of intellectual design we must satisfy three conditions. Firstly, the system needs to adopt a model-centred approach to designing and not pursue the representation of expertise and knowledge at the expense of models. Secondly, the system requires to have tacit knowledge available to support its design decision making, and its dialogue; it is insufficient to work with an unstructured rule or expertise set. Thirdly, and lastly, a system has to be able to accumulate new knowledge from the external world and make that


new knowledge available to subsequent uses of system in a meaningful way; in other words, the system must be able to learn.

The paper opened with a reference to Sketchpad as illustrating an early vision of CAD. We can now look again at Sketchpad in the light of the arguments which have been presented. While it is quite feasible to consider Sketchpad as being powerful because of the constraint methods inposed on geometric models, it can also be considered as a model driven system in which the models have some implicit inferencing capability (or tacit knowledge) and dialogue is supported. This view can be rather exciting for two reasons: firstly it helps to explain the success of Sketchpad, and secondly it highlights a close correlation between what has been happening in CAD and what is being proposed in I-CAD. However, Sketchpad was not attempting to tackle the intellectual activities of designing, and did not have the richness in its models or tacit knowledge to support intelligent behaviour. In addition no mechanisms for self- improvement through learning existed.

But remember the vision for CAD given at the start of this paper.

'It is clear that what is needed if the computer is to be of greater use in the creative process, is a more intimate and continuous interchange between man and machine. This interchange must be of such a nature that all forms of thought that are congenial to man, whether verbal, symbolic, numerical, or even graphical are also understood by the machine and are acted upon by the machine in ways that are appropriate to man's purpose.'

These earliest visions of CAD were almost identical to our present vision of I-CAD. What has changed is that we now have a better understanding of how difficult the task is, and that we now have available to us the knowledge and experience of, and the technology and methodologies from AI and the supporting sciences. We can conclude, therefore, that although I-CAD does not yet exist as a capability, with the support of the research from AI, cognitive science and related disciplines, we should now be able to develop the ability to deliver such systems.

Fig. 5.

lounge

utility room

1 hall /5

kitchen

One possible layout solution

/'5

dining room


Does intelligent C A D exist?: K. J. MacCal lum

ACKNOWLEDGEMENTS

The views expressed in this paper are personal. However, they result from many s t imulat ing and fruitful discussions with past and present colleagues in the CAD Centre. In the prepara t ion of this paper, I would part icularly like to acknowledge the help and encouragement received from Alex Duffy of the CAD Centre, l a n Carter of N E I Parsons, and Tim Smithers from Edinburgh University.

REFERENCES

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