Satisficing in engineering design: causes, consequences and implications for design support

ELSEVIER

Automation in Construction 7 (1998) 213-227

Satisficing in engineering design: causes, consequences and implications for design support Linden J. Ball a,*, Louise Maskill a-1,Thomas C. Ormerod b,2 a Cognitive

and Behaoioural

Sciences

Research

b Department

Group, Division of Psychology,

of Psychology,

Lancaster

University of Derby, Mickleooer,

Unioersity, Lancashire

Derby DE3 5GX, UK

LA1 4YD, UK

Abstract We describe an approach to investigating design cognition which involved comparing prescriptive theories of good design practice with observations of actual design behaviour. The tenet of prescriptive theory which formed the focus of the research is the idea that designers should generate and evaluate multiple design alternatives in order to increase the chances of attaining better design solutions than might arise if they fixated upon an initial solution. Our study focused upon six professional electronic engineers attempting a novel integrated-circuit design problem. Verbal-protocol data revealed: (i) a failure to search for alternative solutions; (ii) a marked inclination to stick with early ‘satisficing’ solution ideas even when these were showing deficiencies; and (iii) only superficial modelling and assessment of competing alternatives when such options were actually considered. We argue that while minimal solution search in design may sometimes be caused by motivational factors and working-memory limitations, its major determinant relates to inhibitory memory processes that arise subsequent to the recognition-based emergence of familiar design solutions. We conclude by exploring the implications of minimal solution search for design support, with particular reference to an agent-based indexing system which we are developing in order to facilitate the pursuit of design alternatives in engineering contexts. 0 1998 Published by Elsevier Science B.V. Keywords:

Engineering design; Cognitive processes:

Satisficing;

Solution search; Design reuse; Intelligent agents; Design support

1. Introduction

1.1. Comparing prescribed and observed as a design-research method

behauiour

The way in which engineers develop solutions to design problems has important theoretical implications for contemporary understanding of problem-

* Corresponding author. E-mail: [email protected]. ’ E-mail: [email protected]. ’ E-mail: [email protected]. 0926-5805/98/$19.00 0 1998 Published PII SO926-5805(97)00055-l

solving expertise in general as well as of design expertise in particular [ll. At an applied level, an analysis of solution-development processes in design is vital for devising optimal methods for design education and sound techniques for computer-based design support [2]. A general approach to investigating goal-directed problem-solving processes which has been applied to good effect in many areas of cognitive psychology is to compare normative theories of optimal practice with what people actually do. For example, numerous investigations of decision making (e.g., Ref. [3]), probabilistic reasoning (e.g.,

by Elsevier Science B.V. All rights reserved.

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Ref. [4]) and deductive inference (e.g., Refs. [5,6]) have examined the extent, nature and causes of people’s deviations from the prescribed norms of optimal behaviour which derive from formal systems such as decision theory, probability theory or propositional logic. Such a comparative approach has much to recommend it. First, in areas such as design where only limited domain-specific psychological theory exists to guide research, this approach enables the investigator to examine behaviour in more than an ad hoc and exploratory manner since interesting predictions can be derived from the normative theory. Second, any commonalitirs between actual behaviour and normative theory lend credence to the possibility that the latter is more than just prescription and may well have descriptive and explanatory merit. Third, any observed deiciations from normative models function to encourage new psychological explanation and investigation-which is valuable for the development of psychological theory within the domain of interest. Deviations from prescriptive principles may also lead to enhancements of normative theory (cf., for example, recent developments in normative accounts of practical reasoning as detailed by Ref. [7]). This kind of comparative approach to investigating cognitive task performance, however, is not immune to potential pitfalls. Perhaps central among these is the possibility that observed deviations from a prescribed norm may lead too readily to ascriptions of ‘irrationality’ to the human problem solver. Such ascriptions have particularly dogged research on human reasoning and decision making (cf. Ref. [S]) although recent work in these fields is increasingly vociferous about the inadequacies of using normative systems as a standard against which to judge human rationality (see, for example, Refs. [9-l I]). Notwithstanding the need for caution in employing a comparative approach in investigating high-level cognition, it clearly appears to be a valuable method in psychological research. Indeed in the design domain, this approach has also been applied-with several key studies having been based on comparisons between normative theories of good practice as espoused in the prescriptive literature and observations of what designers actually do when tackling realworld problems. The majority of design research which has adopted this approach has focused almost

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exclusively on the extent to which designers adhere to the prescriptive principle of optimal solution development as involving a top-down and breadth-first design decomposition. In Section 1.2 of the introduction, we briefly overview the relevant prescriptive and empirical literatures on the theme of top-down design development in order to reveal some of the benefits of the comparative approach in design research. In the present paper, however, our concern is not to explore designers’ pursuit of a structured top-down design principle but instead to examine their adherence to another central tenet of normative design theory, that is, the idea that multiple solution alternatices should be generated and evaluated during the design process (see, for example, Refs. [ 12,131). In subsequent sections of the introduction, then, we discuss the logical rationale behind this prescriptive principle as well as psychological arguments (deriving from Ref. [ 141) for why such a search for alternatives will be a satisficing one rather than an optimising one. We then move on to overview some of the limited empirical work which has actually explored the nature of solution-search behaviours in design. We conclude the introduction by outlining the aims of the study we report in the remainder of this paper which was directly concerned with exploring the solution-search strategies employed by expert electronics designers solving a realistic problem in their domain of professional expertise. 1.2. Empirical ecidence jkr prescriptive breadth-first design

top-down,

Top-down, breadth-first solution development is -in its various guises-one of the key methods of optimal design practice which is espoused in the prescriptive literatures associated with both software design (e.g., Refs. [ 15-181) and engineering design (e.g., Refs. [ 12,13,19,20]). The proposed strength of a top-down, breadth-first design strategy is that it minimises the designer’s commitment to existing solution ideas until all subproblems have been explored at a particular level of detail. As such, then, this limited-commitment mode of processing (cf. Refs. [ 1,21,22]) serves to reduce the amount of redesign work that needs to be undertaken if design ideas are found to be incompatible.

L.J. Ball et al./Automation

Studies which have explored the goodness-of-fit between a prescriptive top-down, breadth-first principle and expert design behaviours have, however, claimed conflicting results. Pioneering research in the software domain (e.g., Refs. [23-261) provided evidence that expert design behaviour concurred well with the top-down and breadth-first method advocated by the structured-programming school. In contrast, more recent research in the domains such as software engineering (e.g., Refs. [27-301) and mechanical engineering (e.g., Refs. [ 1,311) claims to have shown that expert designers deviate (sometimes to a marked extent) from top-down, breadth-first design-instead exhibiting so-called opportunistic behaviour. Although the role of opportunism in design is nowadays widely acknowledged, claims regarding the actual extent of opportunistic processing in expert design have not gone unchallenged (e.g., Refs. [32-3411. In particular, it has been argued (see Ref. [34]) that expert designers may mix breadth-first and depth-first modes of solution development in a highly systematic and principled manner. While such mixed-mode processing may give the appearance of opportunistic design, it actually belies a high degree of structured, top-down goal scheduling. Lang and Ormerod [35] similarly argue that expert Prolog programmers appear to exhibit a particular mixed-mode top-down approach which they refer to as a ‘children-first’ control strategy. Such a strategy is claimed to maximise the advantages of breadth-first and depth-first approaches while minimising the disadvantages of either approach when used in isolation. In spite of the current controversy surrounding the precise status and extent of structured and opportunistic processes in design, it remains the case that major insights into the design process have been derived from the pursuit of a comparative approach based around the exploration of normative design principles. Indeed, the most likely and fruitful interpretation of the current state of affairs is that structured and opportunistic processing are both important features of human design strategies. The challenge for future design research is, then, to develop comprehensive theoretical accounts of when, why and how structured and opportunistic processes arise from such factors as: (i) the designer’s level of

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expertise; (ii) the structuredness of the problem; (iii) the cost, time and managerial constraints impinging upon the design process; (iv) the social dimensions of design (e.g., the presence of co-designers); (v> the sources of information available to the designer; and (vi) the notations available for design modelling (see Refs. [36-381 for some promising ideas in relation to these various kinds of influence). 1.3. The search for alternative

solutions

in design

The idea that designers should systematically generate and evaluate a range of solution alternatives during the design process pervades the prescriptive design literature (cf. Ref. 1131). For example, in reviewing well-established prescriptive design methods within the engineering domain (such as those advanced by Refs. [20,39]) Cross [13] states that: . .. the emphases here are on performance specifications logically derived from the design problem, generating several alternative design concepts by building-up the best sub-solutions and making the rational choice of the best of the alternative designs. (Ref. [13], p. 24) The following quotations from a recent source of prescriptive design techniques [ 121 also clearly illustrate the importance of adhering to the principle of exploring multiple solution ideas: Remember, you need as many ideas as you can possibly generate-single solutions are usually a disaster. (Ref. [12], p. 69) Consideration must be given to a wide range of alternatives without prior commitment to any particular alternative. (Ref. [12], p. 221) The rationale behind the espoused principle of multiple solution search seems, again, to relate to the issue of minimal commitment in design. Since the cost of design decisions can be extremely high if they subsequently turn out to be misconceived, it is advisable to defer decision making (such as fixing upon an initial solution idea for a problem or subproblem) until a range of (possibly better) options have been fully explored. It is especially important to

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defer any commitment to design solutions in the early, conceptual design phases since a high-level solution may subsume many weeks or months of more detailed design work. It is worth noting in relation to the issue of solution search in design that the prescriptive literature rarely claims that designers should devote their efforts to the search for truly optimal solution ideas. Indeed many of the prescriptive design texts show awareness of the facts that in many design contexts: (i) the space of possible solution concepts is typically very large (and therefore computationally intractable), and (ii) important design requirementssuch as those concerned with style-may be nonquantifiable (and therefore not open to optimisation via mathematical techniques). Still, however, the emphasis is placed on deriving the best concept from a range of possibilities-even if it is not the true optimum (see, for example, Pugh’s [12]) method of ‘controlled convergence’ on a best alternative). The issue of solution search in design has been considered from a psychological perspective in the seminal work of Simon [14,40] on design problem solving. In line with the prescriptive design literature, Simon also makes a strong case for the importance of designers pursuing alternative solution ideas when tackling design problems and subproblems, as illustrated, for example, in the following quote: problem-solving systems and design procedures in the real world do not merely assemble problem solutions from components but must search for appropriate assemblies. In carrying out such a search, it is often efficient to divide one’s eggs among a number of baskets-that is, not to follow out one line until it succeeds completely or fails definitely but to begin to explore several tentative paths, continuing to pursue a few that look most promising at a given moment. If one of the active paths begins to look less promising, it may be replaced by another that had previously been assigned a lower priority. [14], pp. 143-144) Simon argues very persuasively, however, that such a solution search is almost certainly not an optimising one, since-as acknowledged by the prescriptive literature-the computational resources needed to search for optimal solutions are far greater than can be afforded by the human information-

(Ref.

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processing system (limited, for example, by the relatively small size of its available working-memory capacity). Indeed, Simon believes that the impossibility of attaining optimal design solutions means that designers actively apply a ‘satisficing’ principle by means of which they search strategically for acceptable design solutions and stick with these after only moderate exploration of the full search space. In this sense, then, human solution-search behaviour in design situations is viewed as rational and effective behaviour within the bounds of cognitive-processing constraints (see Ref. [41] for further arguments concerning bounded rationality in human cognition, and Ref. [42] for a philosophical treatment of satisficing). 1.4. Empirical tices in design

euidence for the pursuit

of alterna-

Given that the prescriptive literature attaches great importance to the search for alternatives in design, it is valuable to question the goodness-of-fit between this normative principle of effective design practice and the actual characteristics of designers’ solutiongeneration behaviours. However, while anecdotal reports about the nature of solution generation in design abound, empirical evidence on this topic is somewhat limited. One study which appears to support the design-methodology literature is that reported by Lawson [43] who found that architectural designers made initial explorations of the problem which resulted in the discovery of a variety of possible solutions until one was found which was good or satisfactory. An earlier study conducted by Darke [44], however, suggested that designers use a limited number of key design requirements to identify a single, initial design concept that subsequently directs all further problem-solving activity. Darke’s notion of the ‘primary generator’ in design served to motivate Hillier et al. [4.5] to propose that design is ‘conjectural’ in nature in as much as designers often propose an initial solution concept very rapidly and subsequently use this conjecture to identify further design requirements and constraints. As such, design solutions may in fact be imagined before the actual design problem itself is fully articulated (cf. the research on architectural design by Bucciarelli et al. ]46]). Of course, such a conjectural approach may be limited to more open-ended and flexible design situa-

L.J. Ball et al./Automntion

tions-such as those often associated with architectural design-rather than being a method of solution generation employed when design problems are based around more forma1 and rigid design specifications of the kind which predominate in engineering contexts. Several studies in engineering domains, however, also seem to provide evidence that designers fixate upon initial solution concepts and uncover additional design requirements during the pursuit of such concepts. For example, one study by Adelson and Soloway [26] which focused upon professional software engineers designing an electronic mail system, and another by Kant [47] which involved graduate students designing algorithms for computational geometry, revealed that designers rapidly developed a kernel idea which was subsequently refined during the design process. Yet another study-carried out by Ullman et al. (Ref. [48]; see also Ref. [31])-focused on mechanical engineers and similarly showed that they became fixated upon preliminary solution ideas and revealed little evidence for the consideration of alternative design concepts. This behaviour was seen both at the level of the overall design problem and at the level of each individual subproblem. In addition, Ullman et al. observed that if weaknesses with original design concepts were later uncovered by the designer they were solved by ‘patching’ the design rather than by discarding it and developing a new concept. As Ullman et al. note: “The first idea was almost sacred, and sometimes even highly implausible patches would be applied to make it work.” (Ref. [47], p. 16) Ball et al. [33] derived similar results in a study of pre-expert electronics designers where it was observed that individuals rarely generated and modelled alternative solutions but focused instead upon initial (and often sub-satisfactory) ideas which were iteratively improved until they reached a state of adequacy. 1.5. Aims of the present study The empirical evidence overviewed in Section 1.4 seems to provide strong support for a view of solution-search behaviour in design that does not match well with the prescriptive view of design as requiring the exploration of multiple solution alternatives. It is

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also worth noting that the studies reviewed transcend a number of different problem types within different design disciplines (e.g., architecture, software engineering, mechanical engineering, and electronics engineering) as well as a number of different expertise levels (including undergraduate designers, graduate designers and professional design practitioners). As such, the tendency toward minima1 solution search in design seems to be a relatively robust and reliable one. Still, however, it is noteworthy that no previous research seems to have focused on solution-search behaviours in relation to expert electronics designers. Indeed, studies of expert, professional practitioners seem to be in the minority in any design domain in the set of studies reviewed above-with the primary focus of research being upon university-based designers with typically only a very limited number of years of design experience. A key aim of the study reported here, then, was to focus on the design behaviour of expert (and primarily company-based) electronics designers with several years’ experience in order to explore further the relationship between prescriptive views of solution search and actual design practice. It should be noted that the study reported below was also pursued in order to explore other key aspects of design behaviour (especially the role of structured design development in engineering) which are of limited relevance to the issues of concern in the present paper. The interested reader is, however, referred to Ball et al. [49] for details of preliminary analyses of the data-set in relation to the theme of structured and opportunistic design development in electronics engineering.

2. Method 2.1. Participants The participants in this study were six male electronic engineers. Five of these participants were professional designers from a large, commercial research and development company of national status within the UK. These five designers had been interviewed prior to the study and had been selected for participation since they had a minimum of 3 years of experience of working in professional design situa-

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tions (range: 3 to 20 years). The sixth participant was a design engineer currently working in an academic research context-although he had prior experience of developing commercial designs within a company situation. All participants had experience of working on complex, multifaceted and multilevelled design problems.

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2.2. Tusk The problem used in the study was written by a professional engineer who had experience of specifying similar kinds of electronics design problems. The problem was presented in the form of a typical design specification and it expressed a requirement

You are required to design an integrated circuit that can perform the following computer vision task: Process an area of RAM memory that contains a 2-D TV image. The chip will be sent to X, Y co-ordinate nairs and constants N. M and L. The co-ordinate pairs X, Y Start and X, Y End define the position of a line vector drawn over the memory. The chip then has to perform the following operations: (1) Calculate N co-ordinate pairs along the line vector; (2) At each co-ordinate pair so derived, generate the apporpriate co-ordinates for a line vector drawn at a normal to the given line vector. Using stepping factor L form M co-ordinate pairs (refer to diagram below); (3) At each normal co-ordinate pair, fetch the nearest pixel from memory and summate its value according to the expressions below, generating two data items for each normal vector -g(x) and h(n). These are returned to the host. Note that the image data is 8 bits deep and the image array is at least 5 12 by 5 12 points.

r=M

g(x) =

\

c

x(i,Sin(k. i)

1=1

i=M

h(x) =

T y‘,

xcKos(k.i) \,

i=l

360 Where k = M

and xi1I is the current pixel

M=5

Fig. 1. Problem specification

presented to participants

L.J. Ball et al./Automtion

for the engineer to design an integrated circuit in an image-processing application (refer to Fig. 1). Some of this circuit’s functional requirements were already detailed as mathematical algorithms while others actually necessitated the generation of appropriate, hardware-implementable mathematical algorithms. Other requirements of the circuit, apart from its basic functionality, were also in need of definition in order for a practical solution to be reached. For example, the specification did not explicitly mention that the circuit should be able to operate in real time, although this requirement was implied by the statement that the circuit was needed to ‘perform a computer vision task’.

Table 1 Taxonomic

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2.3. Procedure All participants were studied individually in a quiet laboratory setting. Participants were given spoken instructions requesting that they should spend between 1 and 3 h tackling the task and that they should take their design as far as possible toward an implementable solution. Participants were also asked to think aloud throughout the design session. Finally, the instructions indicated to participants that they should carry out their pen-and-paper work on numbered blank sheets that had been provided. A videocamera set-up was used to make continuous recordings of each designer’s note-making and depictions,

coding scheme for design behaviours

Abstract categories

Detailed sub-categories

of activity

SEL-P: Selecting or scheduling

UND-P: Understanding

problem(s)

problem(s)

DEV-M: Developing

mathematical

solution for problem

DEV-A: Developing

abstract hardware

solution for problem

DEV-D: Developing

detailed hardware

solution for problem

EVL-M: Evaluating

mathematical

EVL-A: Evaluating

abstract hardware

solution for problem

EVL-D: Evaluating

detailed hardware

solution for problem

EXT.I: Externalising

ACQ-I: Acquiring

information

information

solution for problem

onto paper

or searching

OTH-B: Other design behaviours

for information

219

of activity

Selecting problem Re-selecting problem Scheduling problem(s) Understanding functional requirement(s) Understanding input(s) Specifying design requirement(s) Simulating problem scenario(s) Generating mathematical concept(s) Integrating mathematical concepts Generating abstract hardware concept(s) Integrating abstract hardware concepts Generating detailed hardware concept(s) Integrating detailed hardware concepts Evaluating mathematical solution concept(s) Simulating functionality of mathematical Solution concept(s) Proving mathematical solution concept(s) Evaluating abstract hardware solution concept(s) Simulating functionality of abstract hardware solution concept(s) Evaluating detailed hardware solution concept(s) Simulating functionality of detailed hardware solution concept(s) Writing mathematical expression(s) Sketching block diagram(s) Sketching graph(s) Writing notes Reading problem specification Questioning investigator Questioning self Stating intention(s) Commenting to investigator Explaining to investigator Evaluating progress Summarising progress Non-specific time-filling comment

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and a simultaneous aloud verbalisations 2.4. Protocol

audio recording was also taken.

of their think-

coding

The data obtained in this study comprised: (i) continuous audio recordings (subsequently transcribed) of participants’ think-aloud verbalisations; (ii) continuous video recording of participants’ penand-paper workings; and (iii) original copies of participants’ sketches and notes. A protocol-coding scheme was developed which was capable of simultaneous application to the first two kinds of dynamic data (i.e., ongoing verbalisations and associated external manipulations). The coding scheme that was devised (see Table 1 and refer to Ball [501 for full details) was based upon a preliminary analysis of protocol content and was similar in many essential respects to other schemes for coding design activity found in the literature (e.g., Refs. [ 1,481). It differed from such schemes primarily in the extent to which it contained design-specific terminology. The coding scheme embodied a two-tiered categorisation system where abstract codes simply denoted generic categories of design behaviour (e.g., ‘Understanding Design Problem’, ‘Developing Mathematical Solution For Design Problem’, ‘Developing Abstract Hardware Solution Model for Design Problem’ and ‘Extemalising Information Onto Paper’) and detailed codes denoted spec$c instantiutions of such generic activities (e.g., the activity-code termed ‘Developing Abstract Hardware Solution Concept For Problem’ subsumed the behaviours: ‘Generating Abstract Hardware Concept(s)’ and ‘Integrating Abstract Hardware Solution Concepts’. All encoded activities were given a time stamp within the protocol in order to facilitate the derivation of their duration. Whenever solution concepts were being generated, a note was also made on the protocol as to whether: (i) it was an initial solution concept for a particular problem; or (ii) it was an alternative solution concept for a particular problem having moderately to substantially different properties to any of the previous solution concepts that had been generated in relation to the problem; or (iii) it was an alternative solution concept for a particular problem having minimally different properties to any of the

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previous solution concepts that had been generated in relation to the problem. This latter aspect of the coding system was clearly aimed at enabling the quantification of alternative solution ideas that designers generated during their ongoing design processes. In order to gain some insight into the extent of alternative solution-generation activities at different levels of design detail, the protocol-coding scheme was also devised so as to capture distinct levels of activity, including: (i) solution development and evaluation at the mathematical level; (ii) solution development and evaluation at the abstract hardware level; and (iii) solution development and evaluation at the detailed hardware level.

3. Results and discussion 3. I. The quality

qf participants ’ design solutions

The six participants stated that they had found the design problem novel, nonroutine and quite difficult (especially when mathematical solution concepts had to be invoked). All of the designers completed their design work well within the maximum allocated time of 3 h (i.e., IH took 105 min, JO took 101 min, JM took 72 min. JF took 59 min, JC took 55 min, and DS took 49 min). The designers claimed that their solutions were as accurate and complete as their own knowledge and abilities could allow them to be-but admitted that their solutions were not implementable in their current states and that they would benefit from both peer review by colleagues and input from someone with specialist knowledge of the application domain (i.e.. vision processing). Furthermore, JC was the only participant who had manifestly struggled to make any real headway with the task and he halted his work without seriously attempting many aspects of the overriding design specification owing to his self-confessed lack of relevant knowledge. It is not surprising to note that JC was actually the least experienced of the designers studied. with just 3 years of company work behind him. The overall quality of the six design solutions was evaluated by the individual who had written the problem specification. JC’s solution was classified as: sub-satisfactory at the conceptual level; requiring extensive further work before implementation; not

L.J. Ball et d/Automation Table 2 Number of initial and alternative Category

solution concepts

of solution concept

Initial concepts (in relation to design subproblems) Alternative concepts (moderately or substantially different) Alternative concepts (minimally different)

generated

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at the hardware JO

IH

JF

JM

JC

32

45

23

26

23

I

2

1

0

1

2

0

2

1

1

0

0

0

of the observed

solution-

Before moving on to discuss aspects of the data which can inform the issue of multiple solution search in design, there are a few global characteristics of the observed solution development process which are worthy of brief mention. As noted in Section 1.5, the present data-set has elsewhere been subjected to detailed analysis (see Ball et al. 1491) in order to reveal the nature of any structured or opportunistic modes of processing that were arising. These

Table 3 Number of initial and alternative Category

solution concepts

of solution concept

Initial concepts (in relation to design subproblems) Alternative concepts (moderately or substantially different) Alternative concepts (minimally different)

design level

DS

capable of operating in real time; and having little potential for success. All other solutions were classified as: satisfactory or nearly satisfactory at the conceptual level; requiring a large amount of detailed design work before implementation; capable of operating in real time; and having a fair amount of potential for success. These latter five solutions all involved mathematical algorithms based around iterative constructs which could be implemented as abstract hardware components involving accumulators and other arithmetic logic units.

3.2. Global characteristics development process

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generated

analyses revealed that the designers: (i) devoted a considerable amount of their initial design time in optimising their understanding of the given design task (which is in line with a structured, top-down view of design activity); (ii) implemented a very systematic and normatively optimal strategy of topdown and breadth-first solution development; and (iii) showed only a small amount of deviation (whether opportunistic in nature or otherwise) from this top-down and breadth-first approach. This high level of structuredness that was apparent in the design activity of these engineers was especially useful in facilitating the coding of design concepts and alternatives at different levels of design detail. It should be noted, however, that the data showed little evidence for participants pursuing design work at the level of detailed hardware concepts (i.e., DEV-D and EVL-D behaviours; see Table 1) which renders the data incapable of revealing much information about solution-generation activity at this level of design abstraction. For this latter reason, we combined the levels of abstract and detailed hardware design when it came to quantifying the number of initial and alternative ideas that designers were generating at these levels (see Table 2).

at the mathematical

design level

DS

JO

IH

JF

JM

JC

5

8

11

5

10

I

1

0

0

0

1

0

3

3

2

0

1

2

222

L.J. Ball et al./Automation

3.3. The exploration of alternative in electronics design

solution concepts

Solution-generation data for each designer are presented in Table 3 in relation to the mathematical level and in Table 2 in relation to the hardware level. In line with the protocol coding, each table depicts the number of initial solution concepts generated and the number of alternatice solution concepts generated. The later category is split into those alternatives possessing ‘minimally’ different properties to an initial concept and those possessing ‘moderately or substantially’ different properties. These data reveal some striking aspects of commonality across the designers. First, they indicate (see Table 3) that the engineers were generally failing to search for either moderately or substantially alternative-and potentially better-solution concepts at the mathematical level of design abstraction, where algorithmic solutions to the problem were the requisite focus. In the present design context, the mathematical level is the one at which it would be most cost-effective to pursue genuinely alternative solutions-bearing in mind the arguments of Stefik [21] about minimal commitment in design. Instead, however, our expert designers showed a marked inclination to stick with initial mathematical solution concepts even when these were revealing definite signs of inadequacy. Indeed the present designerslike Ullman et al.‘s [48] mechanical engineerstended to iteratively ‘patch up’ mathematical solution ideas in order to make them adequate rather than devoting effort to the search for, and exploration of, a different line of concept development. This patching of solutions ideas at the mathematical level is evident in Table 3, where it can be seen that the designers were generating numerous slightly different solution variants rather than substantially altemative concepts. Second, at the level of hardware concepts, the present data indicate that the designers studied were showing a slight tendency to generate moderately or substantially alternative design ideas (refer to Table 2). The number of such alternatives, however, was small, especially bearing in mind the relatively large number of initial solutions ideas that were being generated at these hardware levels. It is noticeable, too, that at these levels, designers were (like at the

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mathematical level) also showing evidence for the generation of minimally different variants of the concept that had initially been developed in relation to a particular subproblem. Again, such variants were seen to be generated primarily to replace an initial solution idea that was viewed as inadequate, rather than in a systematic attempt to consider and evaluate competing alternatives in order to optimise choices. Indeed, it is noteworthy that at these hardware levels, designers tended to pursue only very superficial modelling and assessment of competing alternatives on the few occasions when such options were actually considered in tandem. The results of the present study clearly support previous findings deriving from the architectural domain (e.g., Ref. [46]), the software domain (e.g., Refs. [25,47]), and the engineering domain (e.g., Refs. [33,48]) which indicate that designers often fail to pursue alternative solution ideas in accordance with the recommendations of prescriptive design theories (e.g., Refs. [12,20]). The present study, in generalising such observations to expert electronics design, also lends further credence to the possibility of developing a generic, domain-independent account of design cognition as advocated, for example, by Goel and Pirolli [l]. While the results clearly indicate a failure of designers to generate and evaluate multiple solution alternatives, we also feel that the observed behaviours do support the general notion of satisficing as espoused by Simon [ 141 since the designers were concerned to make their chosen solutions adequate, even if this was through iterative improvements and the ‘patching’ of inadequacies (cf. Ref. [48]).

3.4. Theoretical search in design

explanations

of minimal

.solution

Given that designers seem generally not to pursue the search for multiple solution alternatives, it remains to consider possible theoretical accounts of this pervasive phenomenon. One possible interpretation is that it reflects a lack of motivation by designers to seek better solutions when congenial and satisfactory solutions are found. In this respect, it is noteworthy that time pressure would be likely to have a major impact on the motivational set of

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designers, leading, for example, to a conscious strategy in which they might trade off the cost of searching for a better solution against the benefit of attaining an adequate solution as soon as possible. One argument against this possibility, however, is that the present designers were asked merely to take the problem ‘as far as they could’ within the given time period, which was actually quite considerable (i.e., up to 3 h). A second argument against this motivational account is that the designers were at all times seen to work on the problem with great enthusiasm, suggesting that they actually wanted to achieve good solutions but were failing for nonmotivational reasons. Indeed, the designers showed genuine frustration when solution ideas were not showing their expected promise, yet still seemed to fixate on these solutions, as if, for some reason, they simply could not let go of them. A second reason why the present designers may not have pursued alternative solutions is that, while they were generally experienced at working on highly complex and multilevelled design tasks, they may have had limited conceptual knowledge relating to the particular problem at hand. For example, some aspects of the problem were highly mathematicallyoriented in nature and at times it was evident that the engineers were having clear difficulties with these parts of the problem. It is possible that, having generated a viable mathematical solution concept, the designers were then reluctant to give it up. While there may be some validity in this argument, it nevertheless fails to explain why the designers also typically showed limited exploration of genuinely different alternatives for nonmathematical aspects of the problem-i.e., at the hardware levels of the design where they self-confessedly possessed a much greater quantity and variety of relevant domain knowledge. A third explanation of why designers fail to generate and explore multiple solution alternatives relates to the issue of bounded rationality that we outlined in the introduction when discussing (e.g., Ref. [ 141) notion of ‘satisficing’ in design by Simon. Simon suggests that since the search for an optimal design solution is computationally intractable, designers will only generate a small number of altematives and choose the most promising one of these. Even within this view, however, there is still an

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emphasis on the need to explore a number of competing options-albeit a limited number. Ullman et al. [3l], however, take the bounded rationality argument one step further in discussing their findings concerning designers’ development of only single solution concepts. They state that “One hypothesis explaining this observation is that multiple solution proposals (especially detailed proposals) are too complex to be handled well by human designers.” (Ref. [3 11, p. 45). Essentially, Ullman et al. appear to be suggesting that the difficulty for designers is not so much the computational resources that are needed to search for solution alternatives, as the computational resources that are needed to deal with altematives once they have been generated. As such, it may be that designers strategically avoid generating alternatives since they feel that they will be unable to manage the informational complexity that may obtain as a consequence. This is an intriguing account of minima1 solution search in design and although we have picked up on it some of our previous research on computer-based design support (see, for example, Ref. [2] for further discussion), it clearly remains an explanation that is worthy of further empirical investigation. The basic idea that fundamental cognitive limitations lie at the heart of minima1 solution-search behaviours in design seems very reasonable given the high information-processing burden that design places upon mental resources. This is especially the case when one bears in mind that designers must be expending considerable mental effort in prioritising and scheduling activities in order to pursue efficient modes of solution development such as top-down design strategies (see Section 1.2). A final reason for minima1 solution search in design that we will briefly outline here is also based around the notion of bounded rationality. This explanation, however, relates not to the issue of information management in design (a la Ullman et al. [31]) but to the inherent properties of the human memory system. To understand this explanation, it is first necessary to consider the issue of how solution ideas are generated by designers in the first place. What has emerged in the empirical literature (cf. Ref. [5 1I) is clear support for the role of the designer’s memory as the primary source of solution concepts. Design theorists argue that skilled designers possess elabo-

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rated and well-organised knowledge structures reflecting design solutions that have been learned or used in previous design situations. Such knowledge structures are variously referred to as schemas (e.g., Refs. [23,33]), prototypes (e.g., Ref. [52]), or plans (e.g., Refs. [53-5.51). These knowledge structures drive the solution-generation process in design through so-called case-based reasoning (see Refs. [56-601) which denotes a process of recognitionbased retrieval of previous solutions based on the information available in the problem (e.g., the presented design requirements and constraints). Given that designers’ solution ideas stem primarily from their own memories via recognition-based retrieval, it is valuable to reflect upon key features of human memory mechanisms, since these may be contributing to the observation of minimal solution generation in design. In this respect, it is striking that many theories of recognition view it as revolving around processes of ‘spreading activation’ within the memory system. The spreading activation theory of recognition is as readily situated within traditional accounts of memory as involving linked schemas (e.g., Refs. [61,62]) as it is within contemporary views of memory as a distributed network of subsymbolic, neurone-like units (e.g., Ref. [63]). The essential idea behind recognition based upon spreading activation is that incoming cues (concerning, say, a few key design requirements and constraints) serve to access associated information in the designer’s long-term memory (including the most salient and relevant solution concepts in relation to those requirements and constraints-if one is present). More than this, however, the mere recognition of a salient solution idea (which may actually be sub-optimal in a current situation) sets up inhibitory activation within long-term memory so as to block the recognition of alternative solution ideas that may be more loosely associated with incoming cues. In general, such an inhibitory mechanism is a beneficial one within everyday cognition since it enables the individual to rapidly focus on the ‘best’ interpretation of a current situation. In design contexts, however, where the ‘current situation’ may actually be very complex and multifaceted (e.g., in terms of the range of requirements and constraints that need to be considered) this mechanism may prove detrimental since it can lead to the rapid fixation upon an initial

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solution idea and the inability to generate substantially different (but possibly better) alternatives. Under this account of minimal solution generation in design, we are suggesting, then, that the designer may well be motivated to find alternative solutions but cannot succeed because of the inhibitory characteristics of long-term memory which enforce fixation on initial ideas-or at best similar alternatives to these initial ideas. While this account is clearly speculative and in need of further development, we feel that it may have good explanatory value. Indeed, one interesting aspect of the account is that it would predict that when alternatives are generated in design contexts they would be likely to be similar to the original concept (since similar concepts would tend to show mutual activation through their associative links) rather than being substantially different in nature (because different concepts would tend to show mutual inhibition through their associative links). This prediction appears to gain support from both the present research and other studies (e.g., Ref. [481X 3.5. Implications qf minimal har,iour for design support

solution-search

be-

In Section 3.4, we progressed toward an interpretation of the observation of minimal solution search in design (including expert design as in the present study) as being an inevitable consequence of the inherent properties of the human informationprocessing system. Design cognition, including the search for and generation of solution ideas, is constrained by system features such as its limited working-memory capacity and a long-term memory system which functions so as to produce rapid, selective and recognition-based interpretations of current situations (including problems and ways of solving them). As such, it is not that designers deviate from prescriptive design theory out of choice, but rather as a consequence of the selective nature of their cognitive systems when dealing with complex, real-world tasks such as design problems. In general, then, we tend to agree with the suggestion of Ullman et al. [48] that: “... it is conceivable that design is such a complex task that there are very few people who do it well. In this case, the methodologists may be correct, and our subjects may need

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to be retrained or provided with better design tools.” In our view, however, it is unlikely that the retraining option is a viable one since it would seem necessary to have more than the motivation to pursue alternative designs if one is to overcome fundamental cognitive limitations. In Section 3.5 of the paper, we therefore turn our attention toward the second option mentioned by Ullman et al.-that of developing tools to assist designers in the development and pursuit of solution alternatives. With respect to the issue of tools to aid the design process, it seems clear that computer-based methods might provide a useful way of facilitating the generation, evaluation and management of multiple alternative design concepts, including the effective and innovative use of prior design ideas. Indeed, one particular interest that we currently have is with the issue of effective computer-aided reuse of design information in engineering contexts (see Ref. [64]). Clearly, the mere provision of an enormous database of information about what solutions already exist for particular domain problems is not in itself a way to facilitate multiple solution generation in a current design situation and it is not a method which we would subscribe to. One key difficulty that needs to be overcome in order for such a database to be genuinely useful is how to facilitate access to the information that is releoant to a current design problem. A related problem is how to encode reuse information into the database in the first place so as maximise its effective retrieval and application in subsequent design situations. A third problem, is, of course, that such a system must embody mechanisms that can actively promote the designer’s consideration of alternative design via intelligent recommendations and assistance. The recent growth of hypertext information systems has led to a number of techniques for retrieval from, and exploration of, extensive repositories of complex and multifaceted information. These include the use of keyword matching (e.g., Ref. [65]), Boolean search (e.g., Ref. [66]), information aggregation and dynamic querying (e.g., Ref. [67]), varying the weighting of information (e.g., Ref. [68]), and using spreading activation to search a network of index codes [69]. While these all represent useful approaches to information retrieval, they all share common features that make them inadequate for

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handling design reuse information, most notably: (i> they fail to address explicitly the problem of assigning indexing classes to information; and (ii> they rely on user-initiated retrieval of information-which does not address the problem of satisficing behaviours in design reuse contexts. An alternative approach to retrieving information from a reuse database which has particular appeals to us is to introduce a degree of system-initiated retrieval, such that the system has the capability to make decisions on the user’s behalf as to when, and from where, to retrieve reuse information during an ongoing design process. What is needed, then, is a technology that can enable the development of such a system. One such technology that is currently gaining ascendancy in human-computer interaction research is that of ‘interface agents’ (e.g., Refs. [70,71]). Interface agents are processes embodied as discrete programs within a computer system which serve to monitor ongoing system and user activities. When a specific set of conditions arises (pre-determined, for example, by the programmer or the user), the interface agent activates autonomously or semi-autonomously to carry out tasks on behalf of the user (e.g., Ref. [72]). In summary, then, interface agents appear to offer a promising technological solution to the need for system-initiated retrieval of reuse information from a design database during an ongoing design session. In addition, their autonomous nature suggests that they may offer a useful mechanism for aiding the encoding of design information in a way that is minimally invasive for the designer. Finally, such agents also have the capacity to engage in multiple information searches, thereby facilitating the presentation of multiple solution alternatives to the designer who is using the system. It is finally worth pointing out in this latter regard that our notion of design reuse is not merely focused on solution reuse-as appears to be the convention in the current design-reuse literature (e.g., Ref. [73])-but rather on the reuse of the actual process that led to a solution as well. In this way, our approach has more in common with proponents of ‘design rationale’ (e.g., Refs. [74,75]) in which attempts are made to improve design productivity by means of encouraging reflective examination by designers of the design process itself (including information concerning the problem specifica-

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tion, solution options, design critiques, nesses and difficulties encountered).

design weak-

Acknowledgements This research was partly supported by an award made under the ESRC’s Cognitive Engineering Research Programme (Grant Reference No: L127 25 1027). We would like to thank Phil Culverhouse for recruiting participants for this study and for devising the problem specification, Jonathan Evans and Ian Dennis for their support during the running of this study, Kenn Lamb who enabled access to the professional engineers in his charge, and the participants who willingly devoted time and effort to this research.

References 111V. Gael, P. Pirolli, The structure of design problem space\, Cog. Sci. I6 (1992) 395-429.

I21 K.D. Baker, L.J. Ball, P.F. Culverhouse.

I. Dennis, J.St.B.T. Evans, A.P. Jagodzinski, P.D. Pearce, D.G.C. Scothern. G.M. Venner, A psychologically-based intelligent design aid, in: P.J.W. ten Hagen, P. Veerkamp, (Eds.), Intelligent CAD Systems 3: Practical Experience and Evaluation, SpringerVerlag, Berlin. 199 I. Decision Making, Plenum, [31 G. Wright (Ed.), Behavioural New York, 1985. P. Slavic, A. Tversky (Eds.), Judgement 141 D. Kahneman, Under Uncertainty: Heuristics and Biases, Cambridge Univ. Press, Cambridge, MA, 1982. Causes and 151 J.St.B.T. Evans, Bias in Human Reasoning: Consequences, Lawrence Erlbaum Associates. Hove, 1989. R.M.J. Byrne, Deduction, Lawrence Erl161 P.N. Johnson-Laird, baum Associates, Hove, 199 I. [71 R. Audi, Practical Reasoning, Routledge. London, 1989. 181J.St.B.T. Evans, Bias and rationality, in: K.I. Manktelow, D.E. Over (Eds.). Rationality, Routledge, London, 1993. [Yl M. Oaksford, N. Chater, Bounded rationality in taking risks and drawing inferences, Theory Psyc. 2 (1992) 225-230. Reasoning, [101 J.St.B.T. Evans, D.E. Over, K.I. Manktelow, decision making and rationality, Cognition 49 (1993) 165187. [I 11 K.1. Manktelow, D.E. Over (Eds.), Rationality: Psychological and Philosophical Perspectives, Routledge, London. 1993. [I21 S. Pugh, Total Design: Integrated Methods for Successful Product Engineering, Addison-Wesley, London, I99 I. [I31 N. Cross, Engineering Design Methods: Strategies for Product Design, 2nd edn., Wiley, Chichester, 1994.

in Construction

7 (/WK/

213-227

[I41 H.A. Simon. The Sciences of the Artificial, 2nd edn., MIT Press, Cambridge, MA, 1981. [ 151 N. Wirth. Program development by stepwise refinement. Commun. ACM I4 (1971) 4. [ 161 O.J. Dahl, E.W. Dykstra. C.A.R. Hoare. Structured Programming, Academic Press, New York, 1972. [I71 M.A. Jackson, System Development. Prentice-Hall, Englewood Cliffs, NJ. 1983. [ 181 C. Ashworth, M. Goodland. SSADM: A Practical Approach. McGraw-Hill, London. 1990. [ 191 V. Hubka, Principles of Engineering Design. Butterworths, London, 19x2. [20] G. Pahl. W. Beitr, Engineering Design, The Design Council. London, 1984. [21] M. Stefik, Planning with constraints, Molgen: Part I. AI 16 (1981) 111-140. [22] V. Gael, P. Pirolli, Motivating the notion of generic design within information-processing theory: The design problem space, AI Mag. IO (1989) 18-36. [23] R. Jeffrieh, A.A. Turner, P.G. Polson. M.E. Atwood. The processes involved in designing software, in: J.R. Anderson (Ed.). Cognitive Skills and Their Acquisition, Erlbaum. Hillsdale. NJ, 1981. B41 B. Adelson, E. Soloway, A cognitive model of software design, Technical Report No. 342, Department of Computer Science, Yale University, lY84. [Xl B. Adelson. E. Soloway, The role of domain experience in software design, IEEE Trans. Software Eng. I I (1985) 1351-1360. [261 B. Adelson. E. Soloway, A model of software design, Int. J. Intelligent Syst. I (1986) 195-213. [271 R. Guindon, H. Krasner, B. Curtis, Breakdowns and processes during the early activities of software design by professionals, in: G. Olson, S. Sheppard. E. Soloway (Eds.). Empirical Studies of Programmers: Second Workshop, Ablex, Norwood, NJ, 1987. [281 R. Guindon, Designing the design process: Exploiting opportunistic thoughts, HCI 5 (1990) 305-344. WI W. Visser, More or less following a plan during design: Opportunistic deviations in specification. Int. J. Man-Machine Stud. 33 (1990) 247-278. [301 S.P. Davies. Characteriaing the program design activity: Neither strictly top-down nor globally opportunistic, Behav. Info. Tech. IO (1991) 173%1YO. [311 D.G. Ullman. T.G. Dietterich. L.A. Stauffcr. A model of the mechanical design process based on empirical data. AI EDAM 2 (1988) 33-S2. knowledge or [32] T.C. Ormerod, L.J. Ball, Does programming design strategy determine shifts of focus in Prolog programming? in: C.R. Cook, J.C. Scholtr, J.C. Spohrcr (Eds.). Empirical Studiea of Programmers: Fifth Workshop, Ablex, Norwood, NJ, 1993. [33] L.J. Ball, J.St.B.T. Evans, 1. Dennis, Cognitive processes in engineering design: A longitudinal study, Ergonomic\ 37 (11) (1994) 1753-3786. [341 L.J. Ball, T.C. Ormerod, Structured and opportunistic processing in design: A critical discussion, Int. J. Human-Comput. Stud. 43 (1995) 131-151.

L.J. Ball et al. /Automation [35] S.M. Lang, T.C. Ormerod, The global and local control strategies of expert programmers (manuscript submitted). 1361 R. Guindon, Knowledge exploited by experts during software system design, Int. J. Man-Machine Stud. 33 (1990) 279304. [37] W. Visser, Organisation of design activities: Opportunistic, with hierarchical episodes, Interacting Comput. 6 (31 (1994) 239-274. [3X] V. Gael, Sketches of Thought, MIT Press, Cambridge, MA, 1995. [39] L.J. March, The logic of design, in: N. Cross (Ed.), Developments in Design Methodology, Wiley, Chichester, 1984. 1401 H.A. Simon, The Sciences of the Artificial, 1st edn., MIT Press, Cambridge, MA, 1969. [41] A. Newell, H.A. Simon, Human Problem Solving, Prentice Hall, Englewood Cliffs, NJ, 1972. [42] M. Slote, Beyond Optimizing, Harvard Univ. Press. Cambridge, MA, 1989. [43] B.R. Lawson, Cognitive strategies in architectural design, in: N. Cross (Ed.), Developments in Design Methodology, Wiley, Chichester, 1984. [44] J. Darke, The primary generator and the design process. Design Stud. 1 (1979) 36-44. [45] B. Hillier, J. Musgrove, P. O’Sullivan, Knowledge and design, in: N. Cross (Ed.), Developments in Design Methodology, Wiley, New York, 1984. [46] L.L. Bucciarelli, G. Goldschmidt, D.A. Schon, Generic design process in architecture and engineering, in: J.P. Pro&en (Ed.), Proc. 1987 Conf. on Planning and Design in Architecture, American Society of Mechanical Engineers, 1987. 1471 E. Kant, Understanding and automating algorithm design, Proc. bit. Joint Conf. on AI, Morgan Kaufmann, Los Altos. CA, 1985. [4X] D.G. Ullman, L.A. Stauffer, T.G. Dietterich, Preliminary results of an experimental study on the mechanical design process, Technical Report X6-30-9, Oregon State University. 1987. [49] L.J. Ball, J.St.B.T. Evans, I. Dennis, T.C. Ormerod, Problem-solving strategies and expertise in engineering design (submitted). 1501 L.J. Ball, Cognitive processes in engineering design, PhD Thesis, Department of Psychology, University of Plymouth, UK, 1990. [51] G.F. Smith, G.J. Browne, Conceptual foundations of design problem solving, IEEE Trans. Syst. Man Cybernetics 23 (5) (19931 1209-1219. [52] J.S. Gero, Design prototypes: A knowledge representation schema for design, AI Mag. 11 (19901 26-36. [53] E. Soloway, K. Ehrlich, Empirical studies of programming knowledge, IEEE Trans. Software Eng. 10 (1984) 595-609. [54] R.S. Rist, Plans in programming: Definition. demonstration and development, in: E. Soloway, S. Iyengar (Eds.), Empirical Studies of Programmers. Ablex, Norwood, NJ, 1986. [55] D.J. Gilmore, T.R.G. Green, Programming plans and programming expertise, Q.J. Exp. Psyc. 40A (19X81423-442. 1561 J.L. Kolodner, Case-based Reasoning. Morgan Kaufmann, San Mateo, CA. 1993.

in Construction

7 (1998) 213-227

227

[57] P. Pu, Introduction: Issues in case based-design systems, AI EDAM 7 (21(19931 79-85. [5X] B. Trousse, W. Visser, Use of case-based reasoning techniques for intelligent computer-aided design systems, Proc. IEEE/SME’93 Int. Conf. on Systems, Man and Cybernetics. Le Touquet, France, 1993. [59] W. Visser, B. Trousse, Reuse of designs: Desperately seeking an interdisciplinary approach, Proc. 13th Int. Joint Conf. on AI. ChambCry, France, 1993. [60] W. Visser, Use of episodic knowledge and information in design problem solving, paper presented at Delft Protocol Workshop on Analysing Design Activity, Delft. Netherlands, 1994. [61] D.E. Rumelhart, Schemata: The building blocks of cognition, in: R.J Spiro, B.C. Bruce, W.F. Brewer (Eds.), Theoretical Issues in Reading Comprehension, Lawrence Erlbaum Associates, Hillsdale, NJ, 1980. [62] J.R. Anderson, The architecture of cognition, Harvard Univ. Press, Cambridge, MA, 1983. [63] D.E. Rumelhart, J.L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, Foundations, Vol. 1, MIT Press, Cambridge, MA, 1986. [64] T.C. Ormerod, J. Mariani, G. Spiers, L.J. Ball, L. Maskill, Promoting innovative design reuse through indexing agents, poster presented at the Intelligent Agents Workshop, Oxford Brookes University, UK, 1995. [65] F.G. Halasz, Reflections on notecards: Seven issues for the next generation of hypermedia systems. Commun. ACM 31 (19881 x36-852. [66] J.H. Coombs, Hypertext, full text and automatic linking, Proc. ACM-SIGIR ‘90, Belgium, 1990. [67] J. Goldstein, S.F. Roth, Using aggregation and dynamic queries for exploring large data sets, Proc. CHI ‘94. Boston, MA, USA, 1994. [6X] M.E. Frisse. S.B. Cousins, Information retrieval from hypertext, Proc. ACM Hypertext ‘89, Pittsburgh, USA. 1989. [69] J. Savoy, Searching information in hypertext systems using multiple sources of evidence. hit. J. Man-Machine Stud. 38 (1993) 1017-1030. [70] B. Adelson, Evocative agents and multimedia interface design, Proc. CHI ‘92, Monterey, USA. 1992. [71] B. Laurel, Interface agents. in: B. Laurel (Ed.). The Art of HCI Design, Addison-Wesley, Reading, MA, 1993. [72] J.A. Sanchez, J.J. Leggett, J.L. Schnase, Hyperactive: Extending an open hypermedia architecture to support agency, ACM Trans. CHI 1 (4) (1994) 3577382. [73] N. Maiden, A. Sutcliffe, Analogously based reusability, Behav. Info. Tech. 11 (1992) 79998. [74] A. MacLean, R.M. Young, V.M.E. Bellotti, T.P. Moran, Questions, options and criteria: Elements of design space analysis, HCI 6 (199 I) 20 I-250. [75] J.M. Carroll. M.S. van Deusen, J. Karat. S.R. Alpert, M.B. Rosson, Raison d’etre: Embodying design history and rational in hypermedia folklore, Research Report RC 18430 (786 131, IBM Research Division, T.J. Watson Research Center, New York, USA, 1992.