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SIGNPOSTING: AN AI APPROACH TO
SUPPORTING HUMAN DECISION MAKING IN DESIGN
MARTIN STACEY
Computer and Information Sciences, De Montfort University
Milton Keynes, United Kingdom
AND
P JOHN CLARKSON AND CLAUDIA ECKERT
Engineering Design Centre, University of Cambridge
Cambridge, United Kingdom
Abstract. Artificial intelligence provides powerful techniques for
formalising the art of engineering problem solving: for modelling
products, describing task structures, and representing problem
solving expertise as inference knowledge and control knowledge.
Signposting systems extend the scope of these methods beyond
automatic design by using them to provide both information and
guidance for decision-making by human designers. By focusing on
tasks and on the dependencies between design parameters,
signposting systems support contingent and flexible organisation of
activities. This paper outlines the application of AI methods
according to cognitive engineering considerations, to develop
knowledge management tools for engineering design. Such tools can
support product modelling, design process planning and capturing
expert design knowledge, in a form that can be used directly to guide
the organisation of design activities and the performance of
individual tasks. A key element of this approach is the incremental
acquisition of product models, task structures and problem solving
knowledge by defining variant cases.
1
Introduction
One way to view symbolic artificial intelligence is that it is the attempt to
formalise the art of problem solving. This view of the AI project, focusing on
knowledge and mechanism, is broader than the behaviour-centred view of AI
as the construction of intelligent systems.
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MARTIN STACEY, P JOHN CLARKSON AND CLAUDIA EECKERT
One important commercial purpose for expert systems is to make explicit
and encode the problem-solving skills of human experts for reuse by nonexperts, for instance in shipboard medical expert systems. This can be
motivated by the desire to preserve and exploit the intellectual assets of a
company whose employees may leave or retire.
But how can we do effective corporate knowledge management in design?
While expert designers have a wide knowledge of facts and examples, their
essential expertise lies in skills for analysing and solving particular kinds of
problems. These include the perceptual recognition and evaluation of subtle
features that defy analysis and computational representation (see Eckert et al.,
1999). In many situations where we want to encode and reuse the skills of
expert designers, using automatic design systems is impossible or yields
inadequate results.
The approach (illustrated in Figure 1) taken to capturing and reusing
expert problem solving knowledge in signposting systems is to combine
knowledge bases created with the knowledge acquisition and knowledge
description techniques of artificial intelligence, with an inference engine
capable of complex pattern analysis and synthesis operations, and solving illstructured problems with incomplete information: a human designer.
Signposting systems provide a human user both with problem-state
information, the task structure and the state of the design, but also with
problem-solving information, task selections and design guidelines.
Inference
knowledge
(Guidelines)
Product
model
(Parameters)
Task
structure
(Tasks)
Knowledge
acquisition
Control
knowledge
(Task selection)
Inference
engine
Completed
design
Figure 1 The Signposting concept
Section 2 introduces the signposting idea in the context of its antecedents.
Section 3 discusses how signposting can be used to manage expert design
knowledge, and points out some cognitive engineering issues have to be
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addressed in developing design support systems. In section 4 we outline a
signposting system that takes an AI approach to knowledge management for
design; and discuss in passing how the signposting approach relates to
problems stemming from the limitations of human cognitive abilities and
organisational practices.
2
Signposting design tasks
The signposting approach to supporting design decision making comes out of
the realisation that many important design processes have structures that defy
conventional linear process descriptions. They involve complex
interdependencies between design choices, so that designers have to estimate
parameter values, backtrack, and repeat some tasks many times before all the
parameters have satisfactory and mutually consistent values, even when what
the parameters are is well understood. Such processes can be complex and
important enough to require systematic planning, and challenging enough to
gain from expert guidance and decision support.
The core ideas of signposting (Clarkson and Hamilton, 1998, 1999)
emerged during a study of a good example of such a process: helicopter rotor
design (Hamilton, 1999; Hamilton et al., 1997). Form, material and
production method are indivisible in a rotor blade, which may be used for
years in diverse environments. About 40 engineers with a wide range of
expertise participated in the rotor blade development project observed by
Hamilton, and no one had a good understanding of all aspects of the design,
despite the fact that the company is a world leader in rotor blade design. This
made project management and planning extremely difficult. While the
fundamental stages of the design process were easy to identify, more detailed
analysis revealed a nest of information dependency loops and intertwined and
repeated tasks. Process-centred modelling techniques such as those presented
by Pahl and Beitz (1996) proved too coarse-grained or failed to capture the
nature of the interdependencies.
Signposting is founded on the hypothesis that in situations involving
complex interdependencies between different aspects of a design, both
general-purpose problem structuring techniques and domain-specific expert
knowledge can be applied to achieve a linear ordering of tasks and design
decisions. This involves creating sequences of tasks to make collections of
interdependent decisions by making estimates of parameter values and then
using those estimates to refine each other until they converge to a set of
satisfactory and mutually consistent values. Signposting embodies the further
hypotheses that designers and managers could benefit from timely advice
from a computer support system both about identifying and choosing tasks,
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MARTIN STACEY, P JOHN CLARKSON AND CLAUDIA EECKERT
and about how to perform them. This is supplied by recognising situations
when tasks are possible, and when guidelines are appropriate.
This assumes that one can identify the design decisions that need to be
made, that is, the parameters of the design. The signposting approach is being
applied initially to variant design tasks, where the form of the product is
known from previous designs, but obtaining the right parameter values is
highly complex. We are now generalising the approach to cover the design of
ranges of similar products, where identifying the parameters of a design and
their interactions is an integral part of designing (see section 3). In addition
we are investigating customisation and change design, again important in the
aerospace industry, where creating new products involves patching or partial
redesign, and estimating the scope of the changes required to achieve a
modification is essential.
2.1 UNITS OF ANALYSIS: PARAMETERS, DEPENDENCIES AND TASKS
Key concepts in the signposting approach are tasks, parameters,
dependencies and confidences, for which we use the operational definitions
described here.
A parameter is an aspect of a design that needs to be determined, and
hence embodies a decision about the design. While parameters may have
single numerical values, we don’t limit the term to such simple cases.
Parameter values may be symbolic, or have internal structure (such as
complex shapes), or be clusters of related parameters that we want to treat as
a unit. Users can model products simply as flat parameter lists, but the more
advanced signposting system described in section 4 is designed to support
hierarchical product modelling. We treat derived information about a design,
that does not embody any decisions, as derived parameters; whether or not
they are included in product models or in task descriptions is an issue of
convenience.
The values of parameters are often dependent on other parameters. A
dependency is a causal or constraint relationship linking one parameter to
another. Dependencies can be one-way or mutual; we treat mutual
dependencies as pairs of one-way dependencies.
The confidence in a parameter value is an indication of how far it is to be
trusted as an indication of the final value. How to describe confidence in a
form both suitable for AI uncertainty reasoning and intuitive and useful for
designers is a subject of ongoing research (see section 4.4). The current running signposting systems use simple unitary qualitative confidence values.
Tasks are the units of analysis of design processes. A task is an activity
that takes the values and confidences of certain parameters as inputs, and
generates or updates the values and confidences of other parameters (possibly
including the input parameters). Task descriptions include confid-ence
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matrices, naming their input and output parameters, and describing the
confidences required for the input values and expected for the output values.
We envisage that the users of the advanced signposting system outlined in
section 4 will describe tasks hierarchically, first creating high level tasks and
then adding the subtasks that are performed within them.
Task:
Low (l)
Rigid-body - refinement of blade load data
- an initial un-proven estimate
Inputs: blade-loads, transfer matrix
Medium (m) - a feasible estimate
Output: blade-loads
High (h)
if
at least medium confidence
in blade-loads
and
at least medium confidence
in transfer matrix
then
at most high confidence
in blade-loads
if rigid-body test positive
Rigid-body
blade-loads
m
transfer matr m
blade-loads
h
m
Inputs
l
m
l
Output
Confidence
mapping
- a final design value
if
at least medium confidence
in blade-loads
and
at least low confidence
in transfer matrix
then
at most medium confidence
in blade-loads
if rigid-body test positive
Figure 2 Confidence Matrix
Signposting systems support decision making within tasks by presenting
guidelines. Guidelines have no fixed form; but they are typically descriptions
of principles, considerations, and problem-solving procedures that are useful
for performing the currently active task.
2.2 DESIGNING WITH INTERACTING PARAMETERS
In many important design processes, performing variant design and change
design, much of the structure of the design is known in advance. Moreover, a
lot of conceptual design proceeds middle-out: major elements are selected
from available products, or imagined in considerable detail based on prior
experiences of similar systems, and the rest of the design is created around
them. In these situations, the parameters of the design are known or can be
identified relatively straightforwardly. What can make such processes
difficult and complex are the interactions between the parameters and tight,
conflicting constraints on the form of the design. Conceptual-onwards process
analyses, for instance following Pahl and Beitz (1996), and the design support
systems based on them, for instance PROSUS (Blessing, 1994) are too coarsegrained for unpacking the activities involved in finding consistent parameter
values. Similarly abstract-downwards approaches, for instance from
Andreasen (1980), and design support systems using functional models, for
instance Schemebuilder (Bracewell and Sharpe, 1996) and MAX (de Vries,
1994), are irrelevant to these problems.
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By using tasks as the unit of analysis, design processes can be modelled
bottom-up from known atomic tasks, or middle-out from larger tasks, where
considering input-output relationships and decomposing them into subtasks
reveals holes in the analysis. This approach enables the analyst to recognise
situations where the same goal can be achieved by alternative task sequences,
and where the same generic tasks are performed repeatedly and in different
situations, for instance finite element analysis or stress calculation. Modelling
tasks in terms of their input-output behaviour enables them to be selected
according to the states of their input parameters. In the prototype signposting
system (Clarkson and Hamilton, 1998, 1999) a task is highlighted as a
productive next step if (a) the users have sufficient confidence in the input
parameters, and (b) if the task will increase confidence in one or more output
parameters. In the interface shown in Figure 3, the traffic light colours red,
amber and green indicate the progress that can be made by peforming a task:
red means ‘insufficient information to do the task’; amber means ‘possible,
but would not advance the design’; and green means ‘possible and useful’.
Figure 3 Signposting interface
Designers do not always need to have high confidence in the accuracy and
reliability of the information they use; they often make provisional decisions
based on partial information, and the results of these decisions are used to
reconsider the decisions and assumptions they were based on. Clarkson et al.
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(1999) discuss an example of the prototype signposting system supporting
this refinement process. Novice designers using the tool were better able to
reach a design solution than a control group without it, and followed a task
sequence closer to that of an expert.
The dependencies between the parameters are used to identify the linear
order of tasks and decisions. But when parameters are directly or indirectly
interdependent, the task ordering contains loops (see
Figure 4). Here heuristics for breaking dependency loops, plus domainspecific expert knowledge, are needed to propose tasks for estimating
parameter values and progressively refining them until consistent and
satisfactory values are established.
a
a
b
a
context
b
(i) Precedence
a
(ii) Mutual dependence
b
(iii) Influence
b
(iv) Independence
Figure 4 Dependency relationships between tasks
In complex real-life design processes, many tasks may be competing for
limited resources, and the order in which parameter values become available
cannot always be predicted. Some tasks may be delayed, or prove harder than
expected, so parameter values may not be generated at the expected times
with the expected confidences. The design process has to be adapted on the
fly. The signposting systems encourage designers to perform the most urgent
tasks that are possible with the information available.
2.3 RELATED APPROACHES TO SUPPORTING ENGINEERING DESIGN
A variety of approaches to computer support for design have used process
models comprising networks of tasks. Traditional workflow systems provide a
framework for controlling business processes and are used to mediate the flow
of responsibility in those processes from person to person and from task to
task (Prasad et al., 1998). This matching of resource to need is suited to well
behaved business processes where a standardisation of procedure can bring
about increases in process efficiency. However, the engineering design
process is not a well behaved process and even in the case of variant design,
where a new artefact is very similar to previously designed products, there
can be considerable changes in the process that generates the design
information (Dong and Goh, 1998). Signposting provides an approach to
manage such processes by enabling a situation driven guidance engine.
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As well as being used for retrieving cases for adaptive design using casebased reasoning (Göker, 1999), networks of dependencies between parameters have been used for computing task networks for activity planning.
Petri net models. McMahon and Xianyi (1996) use petri nets (essentially,
directed graphs whose nodes are functions and arcs are parameter values) to
create parameter driven design process models. In a parameter driven process
the design process defines task sequences which are executed in response to
parameters being available. Contextual task knowledge is not required.
However, such task network models are static in nature and must be carefully
defined for each new type of product.
Design Structure Matrices. DSMs (Steward, 1981), represent information
dependency relationships as cells in matrices whose rows and columns are the
independent and dependent parameters; in this notation the functions that are
nodes in Petri nets are only implicit. They have been used by Eppinger (for
instance Eppinger et al., 1994) and others to generate task networks as
prescriptive process models for management purposes; this involves explicitly
defining precedence order of tasks. The signposting approach extends this by
introducing the notion of parameter confidence as a means to differentiate
between similar tasks and break dependency loops through estimating and
iterative refinement. The dynamic nature of task selection in signposting does
away with the need to explicitly define task precedence. Signposting can also
include the use of requirements and other contextual information in task
selection.
3
A Signposting approach to managing design knowledge
A signposting system combines a variety of different types of knowledge
about how to design the types of product its knowledge base is built for.
The structure of the product. The parameter set constitutes a model of the
form of the product; with the degree of detail and completeness appropriate
for supporting designing. This may, but need not, include functional and
structural decomposition trees.
An outline requirements analysis. The set of requirement and constraint
parameters constitute a model of the environment in which the product will be
embedded, in a form that facilitates requirements gathering and the use of the
requirements in designing.
The network of information dependencies. The network of dependencies
between parameters embodies knowledge about the constraints imposed on
the design process by the structure of the product.
The dynamic task structure space. The task network (and the resources
for extending it at design-time) embody knowledge about how to organise the
design process dynamically in response to contingent events.
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Problem-solving knowledge. The guidelines for performing individual
tasks embody a variety of types of general and task-specific knowledge about
how to design, such as what issues should be considered and which should be
given priority (for instance Design for X guidelines, Huang, 1996), how to
meet legal requirements, what design choices are most likely to be successful
and why, strategies for formulating and solving problems, and so on.
This sort of corporate knowledge is a valuable asset, especially if it can be
articulated for reuse. The activity of articulating such knowledge can be a
spur to clarifying understanding and uncovering hidden assumptions, so a
system that facilitates knowledge formulation as well as the reuse of
previously formalised design knowledge would be a useful tool for knowledge
management in design. In the remainder of this paper we describe how a
signposting system can achieve this goal.
3.1 KNOWLEDGE REUSE BY VARIANT DESCRIPTION
The approach we take is to identify design knowledge, and extend and refine
it, by describing products as variants of earlier products, and tasks as variants
of generic tasks or comparable tasks in earlier design processes. Searching for
similarities and differences first between past designs and new demands, then
on differences in the inputs to tasks, focuses attention on how widely design
knowledge can be applied. Knowledge elicitation by provoking analysis of
scope is applied to combinations of parameters, task sequences, individual
tasks, and guidelines for performing tasks. This approach to reuse suits
situations where companies do variant design, or create ranges of similar
products. It is also suits design by customisation.
The initial signposting systems (Clarkson and Hamilton, 1998, 1999) were
designed for variant design with fixed parameter sets; the research described
here is on extending the signposting approach to support the reuse of design
knowledge across different but comparable products. In using a signposting
system to design a product with a new parameter structure, creating the
product model and identifying the interdependencies between parameters is an
integral part of embodiment design and process planning. In designing such a
signposting system, there is no clear separation between designing a new
product and eliciting design knowledge for reuse.
Reusing designs is a vitally important part of engineering (for instance,
Duffy and Duffy, 1996). As Eckert et al. (2000) point out, selecting and
adapting sources of ideas to meet the demands of new situations is an integral
and inescapable part of all designing. Computer support for reuse has
attracted a very large amount of research using a wide variety of approaches,
and its own conferences (Sivaloganathan and Shahin, 1998). The signposting
approach focuses on reusing tasks rather than on reusing components, and is
thus limited to designs developed using the system (though other reuse
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methods might be used in performing design-time tasks). We assume that
searching the tree of variants to identify the most appropriate design to adapt
is straightforward. If not, useful case retrieval methods have been developed
for case-based reasoning (see Kolodner, 1993; Voss et al., 1996), notably by
Göker (1999).
3.2 COGNITIVE ENGINEERING OF DESIGN SUPPORT SYSTEMS
The term “design” is customarily used in two distinct meanings. (1) Design as
a style of problem solving is characterised by a cycle of problem refinement,
holistic solution synthesis, and solution evaluation (Asimow, 1962). (2)
Design as a process that produces a description of a new artefact, covering all
the different problem solving activities involved in creating the new artefact
description. Developing successful engineering design support systems
requires effective cognitive engineering, so that they fit both human design
thinking and industrial design practices (see Eckert et al., 1999). Knowledgeintensive tools like signposting systems must also be designed so that
knowledge-encoding activities are not only smooth and efficient, but also
clearly cost-effective for the people developing the knowledge bases.
3.3 COGNITIVE ENGINEERING ISSUES: DESIGN THINKING
Some aspects of human cognition that influence how engineers design and
hence what computer support they need.
Premature focus. Designers seldom explore the space of possible designs,
but instead zoom into detailed design of their first satisfactory-looking
conceptual design. Similarly, people often design by modifying similar
products when reconsidering the problem and searching for alternatives would
lead to better products. The primary purpose of prescriptive design
methodologies (most famously Pahl and Beitz, 1996) and design support
systems based on them, for instance PROSUS (Blessing, 1994), Schemebuilder
(Bracewell and Sharpe, 1996), and MAX (de Vries, 1994), is to encourage
designers to consider a wider range of alternatives at a higher level of
abstraction.
Following habitual paths. Experienced designers learn standard
procedures for solving familiar problems, by repeating what works (for
instance, Anderson, 1983). Such expertise increases efficiency but can reduce
designers’ effectiveness if the procedures become less appropriate to a new
situation, without being obviously wrong (Eckert et al., 1999).
Situated knowledge. The recall and use of problem-solving knowledge is
embedded in the contexts in which it is learnt and used (Suchman, 1997;
Clancey, 1997). It can be difficult to generalise knowledge and recognise its
applicability in other contexts. Much of designers’ ‘factual’ knowledge about
SIGNPOSTING: AN AI APPROACH
11
design principles and inferences, while not completely tacit, is only triggered
by specific problem contexts and so is difficult to find and make explicit
(Christiaans, 1999).
Opportunistic problem-solving. As Suchman (1997) points out, people
only make plans in unusual situations, and when they do, don’t follow them
slavishly but use them as another resource for guiding opportunistic action.
Designing is a mixture of plan-driven and opportunistic action (for instance,
Visser, 1994); designers suspend goals and jump between tasks. Computer
systems that impose an ordering on decisions and tasks can disrupt designers’ working practices and force premature commitments to decisions. This
can influence the products that are designed (see Stacey and Eckert, 1999).
Tacit decision-making. Designers typically think about and describe new
designs with reference to previous similar designs (see Eckert and Stacey,
1999). These and carry with them assumptions and expectations about
products and processes, and focus designers’ recall of ideas and experiences.
Thus they cause designers to consider some options and ignore others, for
reasons they are not conscious of. This is particularly important at the early
stages of the design process when designers are working with incomplete and
tentative information.
Limited ability to reason about uncertainty. Humans are extremely bad at
using probabilities in decision making (see Ayton and Pascoe, 1995); and
have problems following probabilistic arguments even if a computer program
generates them for them. Training in probability theory alleviates this
somewhat, but most people find qualitative probability terms easier to reason
with (see Fox, 1994).
3.4 COGNITIVE ENGINEERING ISSUES: DESIGN PROCESSES
Some aspects of industrial design practice, stemming from the complexity and
multidisciplinary nature of modern engineering development processes, that
influence how products are designed and hence what computer support for
design is needed, as well as how design processes should be managed.
Information flow in concurrent engineering. Concurrent engineering
approaches to design increase the number of factors to be considered in
decision making processes, thus increasing both the number of constraints on
design decisions and the number of applicable rules and principles. The need
to reduce lead times exerts pressure to parallelise design tasks and minimise
backtracking and iterative refinement. The signposting approach was
originally motivated by the desire to achieve ‘right-as-soon-as-possible’ where
‘right-first-time’ is intrinsically impossible.
Design revision. Despite the effectiveness of concurrent engineering
methods, major problems can sometimes emerge at late stages including
prototyping and testing, forcing revision of the design with catastrophic
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effects on lead times. And some design processes are still highly iterative,
because of information dependency loops, lack of information, and the need to
evaluate the product through testing.
Loss of the big picture. In big projects, managers may be unable to
maintain a good understanding of all parts of the development process, and
may be unable to understand what the specialists are doing. At the same time,
specialist designers may lose track of how their tasks fit into the context of
the development of the entire product. They know what the people they
interact with do, and what information they require, but not how this
information is used further downstream. For example designing a new
helicopter takes several thousand person-years over about five years and each
designer is only involved in a small part of this process.
Need for different representations. Designers with different specialisms
may have very different thinking styles, interests and responsibilities, ranging
from mathematical modelling to material selection and drawing, so they have
different information needs. So design information needs to be translated into
a variety of different visual representations, that facilitate different types of
design thinking.
Problematic interactions between designers. Designers may have
difficulty communicating their ideas to their colleagues, who may have
trouble seeing the implications for their own areas of responsibility. In the
seemingly simple case of knitwear design, Eckert (1997, 1999) identified
several different reasons why the interaction between knitwear designers and
technicians is problematic, leading to inefficient processes and suboptimal
products. The critical issues were the lack of adequate notational conventions
for expressing essential information, and lack of recognition of the problem.
3.5 COGNITIVE ENGINEERING ISSUES: KNOWLEDGE ACQUISITION
Some aspects of the knowledge acquisition problem that influence the
feasibility of building and using knowledge-intensive design support systems,
and their effectiveness in use.
Translation cost. The effort and difficulty of knowledge acquisition is
partly determined by how much translation is needed from the terms in which
experts think, into forms in which the system can use the knowledge. In
engineering, an important issue is constructing product models in forms that
are intuitive for the users: supporting hierarchical descriptions but not
requiring them when they are not needed for designing.
Situated knowledge acquisition. As we note above, a lot of important
design expertise only surfaces in the contexts where it is used, so the
knowledge acquisition process needs to support both opportunistic acquisition
and searching for situated knowledge.
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Incremental development and maintenance. It is increasingly widely
recognised by expert system developers that knowledge acquisition should not
be treated as a one-time-only process creating a rigid static knowledge
structure, as such a structure will contain errors and go out of date. So
knowledge-intensive systems need to be designed for ease of revision and
maintenance (Menzies, 1999). Indeed, development and maintenance should
not be regarded as separate (Menzies, 1998; Richards and Compton, 1998).
Incremental revision is a lot more feasible if it can be done by the system’s
users rather than by specialist knowledge engineers. A major weakness of the
approach to parameter-driven task modelling taken by Eppinger et al. (1994)
is that it requires an outside knowledge engineer to create a static task
structure.
Cost-benefit ratio for knowledge acquisition. The effort involved in
formulating and encoding knowledge in the system must not only be costeffective for the company, but also seen to be cost-effective for the person
doing the work. So it must either be directly rewarded, or it must be an
activity that itself has direct and immediate benefits besides the encoding of
knowledge for future use.
4
Outline of a knowledge management tool for engineering design
This section describes our ongoing research on signposting systems that
integrate support for process planning with support for task execution. While
the original signposting systems (Clarkson and Hamilton, 1998, 1999) grew
out of practical experience of engineering design, this work is grounded in
cognitive psychology, and addresses the cognitive engineering issues outlined
in the previous section. It uses knowledge acquisition and knowledge
representation techniques to formulate information that is presented, in the
form of parameter and task descriptions, task choices, and guidelines, to a
human designer who acts as the system’s inference engine.
4.1 KNOWLEDGE ACQUISITION BY RIPPLE DOWN MODELLING
We address the need for incremental knowledge acquisition for signposting by
extending the ripple down rules method of developing expert systems,
developed by Paul Compton and colleagues at the University of New South
Wales (Compton and Jansen, 1990; see for instance Richards and Compton,
1998; Menzies, 1998, 1999). In situations where the existing ruleset is not
adequate, a new rule is added as a variant of an existing rule.
Product models comprise sets of parameters. The user is free to describe
products with flat lists of parameters. But hierarchical product models can be
developed incrementally top-down, bottom-up, or middle-out, (1) by
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redefining a general parameter as comprising a set of child parameters; and
(2) by adding links to one or more super-parameters to an existing lower-level
parameter, to assert that it is part of a larger structure. Product models for
variant designs and similar products are created using ripple down modelling.
Only the parameters that are different (usually because they have different
decompositions into subparameters) are defined separately; the new variant
parameters are created by modifying the root-variants, and have links back to
them, and rationales for splitting them off.
Similarly the user is free to create flat lists of tasks, or create a task
decomposition hierarchy by iterative refinement. Variant tasks for different
situations (at different points in one product or in different products) are
created by modifying root-variants, with rationales for making the splits
including a description of the scope of applicability of the task. As
engineering design involves many standard activities used in many different
situations, such as finite element analysis, tasks can be created by drawing on
a stock of generic tasks not associated with any particular product.
As tasks are specified in terms of input and output parameters, attention is
focused during knowledge acquisition on information needs, and on the
assumptions about design choices and task performance implicit in information choices. Considering input and output of information also focuses
explicit attention on how different members of design teams performing
different tasks need to exchange information, and thus on situations where
people are providing insufficient or inappropriate information. It also highlights situations where there is a mismatch in the form of the parameter
values, and where automatic translation between alternative representations
would be useful. Considering task sequences encourages users to make
standard procedures explicit, and so reconsider their appropriateness.
A guideline for task performance is associated with a task – its scope.
When a new variant task is created, it inherits the guidelines belonging to its
root. The user can create variant guidelines for the new task (including null
forms if guidelines are simply inappropriate for the new variant task.)
Although creating and modifying guidelines is not an intrinsic part of
creating and varying product models and task structures, ripple down
modelling focuses attention on the similarities and differences between task
situations. It thus creates appropriate conditions for recalling and encoding
situated task-specific problem solving knowledge.
4.2 A TOOL FOR PROCESS PLANNING AND DESIGN GUIDANCE
In the signposting approach, identifying the parameters of a new design (that
is, developing a product model) is tightly bound to identifying the tasks
needed to design it. An essential requirement for a signposting system that can
be used for new types of product, is that it should function as a useful tool for
SIGNPOSTING: AN AI APPROACH
15
process planning; and moreover that knowledge acquisition for task
performance guidelines should be a relatively painless extension of this.
Thus a signposting system has build-time and design-time modes,
corresponding to two functions of a human design manager, first planning the
expected tasks and assigning resources to them, and then coordinating the
process and guiding the activities of junior team members. At build-time, the
user constructs both a product model and an outline task structure by
identifying the parameters and the dependencies between them. Build-time use
of the signposting system can be interleaved with design-time use. What
parameters the design has may depend on earlier design decisions (that is, the
values of other parameters). Similarly, appropriate tasks and task sequences
for later stages of the design process may have to be planned according to the
results (parameter values) from earlier tasks.
4.3 DESIGN GUIDANCE
In a signposting system, design-time guidance takes two forms. The first is
highlighting tasks that are possible with the available information (see section
2.2); we are working on extending this with user guidelines and a back-end
reasoning module for identifying the most urgent tasks. This approach
facilitates flexible opportunistic task choice, while focusing attention on
productive actions and warning against attempting tasks with inadequate
information.
The second form of design-time guidance is through guidelines that are
presented when the user undertakes a task. These can include reminders to
check facts, possible solution principles, strategic problem-solving advice,
methodological advice, and design for X guidelines. How guidelines should be
presented to the user is an open research question. In the present
implementations, the system presents a list of applicable guidelines in a
special window. Specifying the scope of guidelines is an essential part of the
signposting approach. Even so, for some tasks there may be an infeasably
large number of guidelines, so we are considering taking a critiquing
approach (see Silverman, 1992; Fischer et al., 1993) to select guidelines
according to the state of the design, and using user models to choose
guidelines according to a designer’s expertise and experience. Research on
how active critiquing systems are used reveals that the timing of advice can
influence designing behaviour (Lemke and Fischer, 1990).
4.4 SUPPORT FOR HUMAN UNCERTAINTY REASONING
A design support system dealing with situations where designers work with
uncertain information should propagate uncertainty information and assist
with probabilistic reasoning. It should also enable designers to use terms for
16
MARTIN STACEY, P JOHN CLARKSON AND CLAUDIA EECKERT
uncertainty information they find intuitive and easy. The choice of method
depends on the nature of the uncertainty and the task. Signposting systems are
concerned with using uncertain information as inputs to user-performed
processes to derive uncertain outputs. The existing signposting
implementations use simple qualitative estimates of how much a value can be
trusted as being close to its final value, with a simple non-Bayesian
propagation algorithm.
However what information engineers want and can generate about the
trustworthiness of parameter values, and how it can be propagated using AI
uncertainty reasoning techniques, are open research questions, which we
intend to explore experimentally using signposting systems as testbeds. The
following uncertainty concepts are potentially significant in design thinking.
Confidence. The degree to which a parameter value can be relied as being
satisfactory in relation to the parameter values and constraints from which it
was generated.
Commitment. The degree to which the project is committed to keeping the
current value of the parameter (conversely, how easily it can be changed to
meet other needs).
Precision. How exact the value is. (Does x=10 mean 9.998<x<10.002 or
8<x<12?)
Sensitivity. How far the value can be changed without significantly
affecting the rest of the design, and the consequences of changing it more than
that.
Understanding. The extent to which the user has sufficient information
and expertise to derive the parameter value from the input information.
4.5 PROPOSED SYSTEM ARCHITECTURE
Figure 5 shows a system architecture for a signposting knowledge
management tool employing a blackboard architecture to handle interaction
between different interface modules and reasoning modules. Build-time
functionality is shown on the left, and design-time functionality on the right.
The tasks including their confidence mappings are stored in the task base,
which also includes product-independent generic tasks. The task hierarchy
display shows how higher level tasks are composed of sets of lower level
tasks. The user records information about the active task using the task
interface. The user can select a task in the task selector, which displays the
currently possible and useful tasks. The signposting engine identifies tasks
that are possible with the available information. It generates the task map –
equivalent to a petri net. This can be displayed to show the interactions
between the tasks through their input and output parameters and their current
states. When the system has a hierarchical product model and a hierarchical
task structure, the task map can be generated for different levels of detail. The
SIGNPOSTING: AN AI APPROACH
17
task hierarchy display and the task map helps the user understand the context
in which particular tasks are carried out, and see how the task structure
handles information dependency loops and iterative activities.
Editor
Database
Guideline
editor
Guideline
base
User interface
State of
the design
Guidelines
Guideline
handler
Guidelines
Guideline
display
User model
Product model
Requirements
editor
Requirements
base
Parameter
editor
Product model
Product model
Guidelines
Product
model
Task display
Task hierarchy
Parameter
base
State of
the design
Task
editor
Task
base
Missing
tasks
Task map
Task
proposer
Task
selector
Next task
Next
task
Task map
Tasks
Parameters
Version
manager
Task
hierarchy
Possible
tasks
Signposting
engine
Tasks
Version
base
Product
display
Version
description
Task
handler
Design
rationale
Parameters
Task
map
Task
interface
Design
rationale
Tasks
Product model
Design decisions
Building
Using
Figure 5 System Architecture
The structure of the design (the product model) is stored in the parameter
base and requirements base. (Parameters and requirements are essentially the
same thing for knowledge representation purposes, but are differentated here
for conceptual clarity.) The version base holds the variant selections and
individual parameter values for particular designs. The version manager
controls interaction with the version base.
Parameters, requirements, tasks and guidelines are created and varied with
their own editors. The task proposer uses the dependencies between
parameters and the input-output specifications of existing tasks, to identify
missing tasks, and to recognise information dependency loops. It applies
general task structuring heuristics to propose linear sequences of tasks for
estimating and refining interacting parameter values.
There are no limits to how much the task interface can be customised for
individual tasks. Useful functionality for frequently-used generic tasks would
include special purpose visual representations of aspects of the design, and
calls to appropriate CAD systems. The only restriction is that the tasks should
generate output descriptions comprising parameter values that are in the form
18
MARTIN STACEY, P JOHN CLARKSON AND CLAUDIA EECKERT
expected by later tasks, and follow the system’s conventions for describing
confidence in parameter values.
Callouts to AI modules and other programs for performing algorithmic or
computationally tractable design activities are treated as individual tasks, that
are created at build-time using the task editor, and are proposed at designtime when the necessary inputs are available.
5
Conclusions
In this paper we have outlined the signposting approach to using artificial
intelligence techniques for modelling design knowledge in forms that human
designers can use. This knowledge takes the form of models of the structures
of products as sets of parameters and the dependencies of parameters on each
other; the tasks required to generate parameter values; and guidelines for
performing the tasks. The tasks are derived from the parameter dependencies
using general heuristics and task-specific knowledge to propose task
sequences that break information dependency loops by iteratively estimating
and refining parameter values. In this approach specifying the outline form of
the design is tightly coupled to process planning.
The signposting approach avoids specifying the form that the
representations of a design should take, or even imposing the requirement that
it should have a coherent hierarchical structure.
By adapting the ripple down rules method of expert system development
(Compton and Jansen, 1990), knowledge bases for product models, tasks, and
design guidelines can be developed incrementally as a part of the concept
selection and process planning stage of design. By focusing attention on the
input information required by design activities and the output information
they generate, planning the design process by specifying tasks not only
encodes corporate knowledge of how to design, but encourages designers to
make explicit situated knowledge and tacit skills.
Acknowledgements
This research has been supported by the EPSRC rolling grant for the Cambridge University
Engineering Design Centre. It has benefited from conversations with other members of the
Signposting project at the Cambridge EDC: Jamie Hamilton, Andy Connor, Jerome Jarrett
and Andres Melo; as well as with numerous engineers at GKN Westland.
SIGNPOSTING: AN AI APPROACH
19
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