A Case-Based Approach to the Managementand Deployment of Knowledge Assets
Rick Magaldi
British AirwaysInformationManagement,
CorporateBusiness Solutions
Waterside, POBox 10, HeathrowAirport, Hounslow,Middlesex, UK
Rick.V.Magaldi@British-Airways.com.uk
From: AAAI Technical Report WS-99-10. Compilation copyright © 1999, AAAI (www.aaai.org). All rights reserved.
distinctions made between commonsense and specialised
expertise. Knowledge
in this context can be categorised as
either tacit, explicit or cultural [Choo1999].
Abstract
This paper outlines howcase-basedreasoningtechnologymay
satisfy certain outstanding requirements in the field of
knowledgemanagement(KM). The innovative handling
tacit andcultural knowledge
is a keyfactor that will definethe
future success of commercialorganisations. The growing
recognitionthat KM
is principally peoplefocusedshouldgive
rise to a technologicalinfrastructurecapableof supportingthis
requirement.Case-basedreasoningis ideally placed to handle
the storage, indexingand retrieval of both tacit and cultural
knowledge.A case study is described that deals with the
production and deploymentof a case-basedreasoning system
for the British AirwaysConcordeOlympuspowerplant. The
system makesavailable the collective past experiences of
Concorde
engineeringstaff to assist in problemsolving.
Tacit knowledgeconsists of the learning and know-howan
individual accumulates through experience. Explicit
knowledge consists of what has been declared and
formalised in some way. Examples include, operating
manuals and safety procedures. Cultural knowledge
consists of what individuals communicate through both
formal and informal means. Much of
what is
communicatedin this way is not recorded, but is sustained
verbally. It is a very important mediumof knowledge
exchangeand determines howsuccessfully the organisation
functions on a daily basis.
Introduction
Many of the challenges
faced by service-based
organisations operating within the current marketplace can
be characterised
by a need to compete on quality,
products, customer service and innovation. There is a
growing recognition by many such institutions,
that in
order to achieve these goals, there is a need to move
towards a more knowledge-basedbusiness infrastructure.
This in turn creates a need to understand new ways in
which knowledge, people and technology can be
harmonised to ensure business success [Barr and Magaldi
1996].
A great deal of effort has been expended within the
business world to make knowledgeexplicit for the benefit
of the organisation. Expert systems have been deployed
with varying degrees of success, and mucheffort has been
devoted to knowledge elicitation
methodologies to
facilitate
the harvesting of tacit knowledge. Such
approaches will continue to be useful in certain domains,
but a more flexible approach is required to meet the
technological infrastructure requirementsfor the successful
managementand deployment of knowledgeassets.
The most efficient
means of supporting knowledge
workers of the future will require the co-operation of
several technologies rather than one. These will support a
knowledge sharing environment rather than one that is
strictly knowledge-based. The core infrastructure
to
support knowledge sharing is already available on the
Internet and Intranet. Electronic Mail and Groupware
provide the first layers of a knowledgemanagement
system
(distribution
level). The actual knowledge layer
(knowledge storage, indexing, and retrieval) shown
Figure 1, is difficult
to control by means of more
traditional
information systems as they are largely
concerned with the logistics of data. Approachesthat will
enable the knowledgelayer to becomea reality rather than
remaining an abstract concept will include a mixture of
data-mining, neural computing, agents, and case-based
reasoning technologies. As part of this process, attention
The management of knowledge as a business asset is
clearly going to be high on the agenda of corporate
planners as restructuring of organisations is forced by
rising costs. What form these changes will take in both
practical and economic terms is still being debated by
industry analysts. However,what is already clear is the
need for an efficient means for the identification,
refinement, storage, indexing and retrieval of key business
knowledge. This will be required to attain the goals of
efficient
knowledge sharing and of just-in-time
knowledgecapture within business.
Organisational knowledgestructures can be very complex,
requiring detailed models of processes both within the
companyand the outside world. These must combine both
spatial and temporal elements of experience, with
46
mayhave been forgotten can be utilised, and relearning of
what has already been experienced avoided.
must be given to handling all types of knowledge,
including textual, graphical, and pictorial forms.
Within British Airways case-based reasoning is being
investigated as one potential means of managing its
knowledgeassets [Burchell 1997]. These can be described
as the insights and experiences gained by the organisation
with regard to the products, technologies and markets that
British Airways owns and uses to generate revenue. One
area at an advancedstage is the application of case-based
reasoning techniques to aircraft maintenance.
Knowledge
Infrastructure
Aircraft Maintenance
Figure 1. Knowledge
sharing via Intranet and Internet
Because case-based reasoning is grounded in a model of
humancognition based on concrete experience rather than
on dry abstractions it is natural and easy to grasp. It has an
intuitive appeal to manyof those involved in knowledge
management and is therefore gaining acceptance as a
foundation KMtechnology (e.g. [Watson 1997]) from
many organisations. A recent application example that
combines case-based reasoning, relational database, and
web technologies has been produced by Western Air Ltd.
of Fremantle, WesternAustralia.
They have deployed a case-based reasoning system that
operates on the world wide web. It supports a sales force
deployed over a wide geographical area whoare required
to produce quotations for complexair conditioning system
configurations. The benefits derived to date include
increased profits ($1M), increased sales staff capability,
release of experts to focus on more complex tasks, and
increased customer satisfaction [Gardingen and Watson
1998]
Case-Base Reasoning
The basic assumptionbehind case-based reasoning is that if
something has been experienced in the past, there maybe
features of that experience that can be utilised to solve
somecurrent problem or aid understanding of sometask or
other. The general approach is to avoid fundamental
problemsolving by taking more direct means. The strategy
is dependenton the availability of someefficient meansof
storing useful experiences, and indexing these for later
retrieval by meansof an efficient retrieval engine.
A case-based reasoning system therefore enables
remembering from experience to take place. This is
achieved by retrieval of those experiences most similar to
somecurrent situation. By this process past insights that
47
Airline operating schedules require that swirl and efficient
problemrectification takes place after aircraft defects are
reported, if expensive delays are to be avoided. In such
situations, the maintenance engineer is very likely in
addition to the use of maintenancemanuals, to apply rich
insights gained from experience, and in doing so, better
gaugethe utility of certain diagnostic approaches,or assess
the merits of particular
maintenance actions. An
experienced person is likely to ask such questions as "have
I seen this problem before?", and "what produced an
effective fix to this type of problem?"~
However, the uniformity of this type of approach can be
very variable, due to a variety of factors including staff
inexperience. It would be extremely helpful under these
circumstances to be able to have a repository of diagnostic
experiences that can be madeavailable to all engineering
staff, including inexperienced engineers. For this reason
case-based reasoning is being applied to support these
requirements, and by doing so, to ensure that aircraft can
be consistently and expertly maintained. The overall effect
of this will be to minimise departure delays due to
engineering problems and lower operating costs [Magaldi
1994).
Current case-based reasoning work within British Airways
Engineering is focused on support to diagnostics on the
Concorde Olympus powerplant. This is a very complex
assembly, comprising of a variable geometryintake system,
main engine assembly, reheat system and reverse thrust
assembly. Accurate diagnostics and repair procedures are
essential to mitigate the effects of departure delays (these
cost £1000 per minute) due to engineering problems, and
maintain a cost effective operation, with a requirement for
a very high standard of passenger service.
To meet these needs, British Airways has produced a
prototype case-based diagnostics
system based on
commercially available
software from CaseBank
Technologies IncTM. of Canada. The software is called
SpotLightTM, and is optimised for aircraft maintenance
diagnostic tasks. It runs under Microsoft Windows
95TM or
taken place operationally since 1984. The following types
of problemsare currently represented:
NT 4.0 TM and is capable of links to any ODBC
compliant
database. Additional features include communicationswith
programs using COMor OLEstandards, works on LANs
(TCP/IP support) WANs,
or as a stand-alone system. It can
also support dial-in or internet access.
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The current system configuration being deployed by British
Airwaysis a stand-alone system for the duration of current
operational trials. However,it is hopedto extend this into a
networkedmulti-user facility in the future.
SpotLightTM OlympusDiagnostic Tool
The SpotLight TM case-based Olympus engine diagnostic
tool assists maintenance technicians in solving problems
with Olympus engine systems or equipment more
efficiently, by telling themwhethera problemsimilar to the
one that they are facing has already been solved at another
place or time.
Fire Warning& Extinguishing
Intake Failure
Low Power
Oil System
Rapid Acceleration
Reheat Failure
Reverse Failure
Surge
Fuel
Vibration
The system allows a user to input engine fault symptoms
and associated observations in response to system directed
questions. This is achieved by using a mouseto point and
click on screen icons and hot-spots. Very little keyboard
use is required. The purpose of this dialogue is to provide
the user with a list of cases containing the most likely
causes of the observed symptoms- the most likely being
listed first. The system is considered as ideal for use in
time-constrained situations, and should help reduce
SpotLightTM facility has three major components:the casebase, the software that searches the case-base, and the user
interface. Previously solved problems and their solutions
are stored as cases.
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Figure 2. SpotLightTM ConcordeOlympusDiagnostic Tool
Figure 3. MainDiagnostic Session Window
(initial state)
A User can simply point and click on the symptomsthat
best describe the current problem. SpotLightTM will ask
pertinent questions to differentiate amongpossible cases
and bring forwardthe cases in the case-base that best fit the
problem.The user can then decide if the solution in any of
the cases presented by SpotLightTM is the right one for the
current problem.
unproductive time spent during fault diagnostics,
particularly whentimely decisions have to be made.
The system, like a human,should also improve in accuracy
as experience increases. This will be achieved in
SpotLight TM by constantly adding new cases that have
successful outcomes,as they occur.
A seed case-base has been constructed around 270 cases
dealing with a variety of operational defects relating to the
Concorde Olympus powerplant and installation.
These
cases represent reported and recorded events that have
A Typical Consultation
A diagnostic session begins by selecting the appropriate
diagnostic area, e.g., Intake Failure, LowPower, Oil
System. On the basis of this selection, the system will
48
respond by introducing a list of questions to be answered.
These questions are those that the system has judged most
suitable for initiating a useful diagnostic dialogue for a
particular diagnostic area. On answeringa given question,
a search can be initiated by clicking on the ’search’ button,
or several questions answeredat once before this is done.
Whena search is initiated, the system search engine
extracts those cases that most closely match the evidence
so-far presented, and excludes those cases that have no
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Figure 4.
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Consultation
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) illumin~ed?
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answering further questions. Cases displaying high
similarity scores can then be selected and their histories
examined. Selection is achieved by ’highlighting’ the
particular case to be examined, and then activating the
’view" button. The process is repeated for each case of
interest and can be carried out in any order. Also, questions
previously answered can have their values changed and
newsearches carried out.
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9
i?
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example (similarity
Figure 5. Consultation example(similarity scores at or near
100%)
scores
matchingcharacteristics.
So, from having a large number of cases initially,
the
potential numberof cases of interest is reduced through a
series of steps. Finally, a few cases remain, these being
judged by the system as having the highest matching
characteristics with the current problem.
Ranking of the matched cases is done numerically (1100%), and by colour coding, red, yellow and green. Red
meansa low score, yellow is mediumand green is high. In
practice, Yellow and green scores signify cases that are
worthlooking at, rather than just green alone.
It is important to note that when lists of questions are
generated by the system, these can be treated in any order.
Also, there is no obligation to answer them all, only as
many as cover observed symptoms. Figs. 3. & 4. show
examples of SpotLight TM on-screen information.
A
consultation session can be interrupted at any time and
saved for later progress. A saved session remains the
secure property of the user until it is shared for general
viewing,or it is cancelled.
Figure 6. Case Information Window
Whenexamining a particular case, a new windowbecomes
available containing a complete range of information about
that case, Fig. 6, and includes:
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SymptomObservations
Causes
Repairs
Explanations
Each case follows a commonformat making it simple to
comparedifferent cases and their outcomes.
On completion of the initial question and answer session,
the significant cases can be openedor search continued by
49
Conclusions to Date
Initial assessment of case-based reasoning technology
within British Airways has been very positive. The
SpotLightTM diagnostic facility is now being extended to
incorporate more of the events (potentially 2500+) that
have occurred over a period of 20 years. These are being
introduced to the system over a six month evaluation
period in consultation with the user. In parallel with this,
the Olympussystems model used by the reasoning engine
is being refined with the help of Concorde engineering
experts.
Steve Mott, Powerplant Performance Engineer, for their
valuable technical contribution, enthusiasmand support to
the aimsof the project.
This hands-on participation by experts in system build and
refinement, gives a great deal of validity to the knowledge
output from the system. It is essential that user community
have confidence and trust in knowledgesharing systems of
the type being described.
Choo, C. W. The Intelligent Organization: Mobilizing
Organizational Knowledgethrough Information Partnerships.
1999.Facultyof InformationStudies, Universityof Toronto.
Also being given attention at this stage is the development
of a return-on-investment plan that will help British
Airwaysassess the key financial benefits to be achieved
from the current and future application of case-based
reasoning technology. The experience gained on the
SpotLight TM project will be used to drive other
programmes.
The strategy for the broader deployment of case-based
reasoning technology within British Airways is being
developed, and is one that will initially focus on the
logistics and economics of building, validating and
maintaininglarge case-bases. It is realised that for existing
knowledge sources to be exploited, methodologies,
standards and procedures need to be developed. The
ultimate success of first-generation case-based reasoning
systems supporting knowledge managementwill depend on
the facility to automatically, generate cases from diverse
data sources, thus eliminating the need to enter large
numbers cases by hand.
References
Barr, J. M., Magaldi, R. V. Corporate Knowledge
Management
for the Millennium. In Advancesin Case-BasedReasoning,
Smith, I., Faltings, B. eds. 1996. Lecture Notesin Artificial
Intelligence1168.Berlin.: Springer-Verlag.
Burchell, B.1997, Computerson the case. WorldEngineering
July/Aug:42-43.
Gardingen, D., Watson,I. A WebBased CBRSystemfor HVAC
Sales Support. In Applications and Innovations in Expert
SystemsVI, Milne, R., Macintosh,A., &Bramer,M.eds. 1998.
Proceedings of Expert Systems 98. Cambridge. : SGES
Publications.
Magaldi, R.V. Maintaining Aeroplanes in Time Constrained
OperationalSituations using Case-BasedReasoning.In Advances
in Case-BasedReasoning,Hatton, J. M., Keane,M., &Manago,
M. eds. 1994. Lecture Notes in Artificial Intelligence 984.
Berlin.: Springer-Verlag.
Watson, I. Applying Case-BasedReasoning: Techniques for
Enterprise Systems.1997. MorganKaufmann.
Finally, British Airways Concordeengineering staff have
recognised the case-based reasoning approach as one
complementary to their own way of thinking and problem
solving. Users currently involved in trials and evaluation of
the SpotLightTM system, are very enthusiastic about the
potential for such systems in the future, and have now
requested that other Concordesystems be considered for
inclusion within a future facility.
Acknowledgements
author thanks Phil D’Eon CEO,and the SpotLight
development team of CaseBank Technologies Inc TM, for
their continuous support to the Concorde Olympus
programme. Also, the following British Airways Staff.
Steve Lincoln, Concorde Fleet Technical Engineer and
TM
The
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