ICT619 Intelligent Systems Topic 7: Case Based Reasoning

ICT619 Intelligent SystemsICT619 Intelligent Systems

Topic 7: Case Based Topic 7: Case Based ReasoningReasoning

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Case Based ReasoningCase Based Reasoning

IntroductionIntroduction How CBR worksHow CBR works Business Applications of CBRBusiness Applications of CBR CBR Development methodology and CBR Development methodology and

ToolsTools AdvantagesAdvantages of CBR systemsof CBR systems

Case StudyCase Study

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What is Case Based Reasoning?What is Case Based Reasoning?

An intelligent systems methodology based on using An intelligent systems methodology based on using stored past problem solving experience to solve a stored past problem solving experience to solve a current problemcurrent problem

Similarity with human problem solving - analogical Similarity with human problem solving - analogical reasoning or memory-based reasoningreasoning or memory-based reasoning

CBR draws on similarities and differences between a CBR draws on similarities and differences between a given problem and a similar problem solved in the pastgiven problem and a similar problem solved in the past

CBR learns from experienceCBR learns from experience Adds a solved problem to the case base for use in Adds a solved problem to the case base for use in

future future Difference with ANN learning - does not generaliseDifference with ANN learning - does not generalise Difference with rule-based systems - cases are not Difference with rule-based systems - cases are not

rulesrules

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Case Based Reasoning TermsCase Based Reasoning Terms

A A casecase denotes a problem situation, experienced or learned in the past and denotes a problem situation, experienced or learned in the past and retained in a retained in a case basecase base

A case may also be an unsolved case – a problem to be solved at present or in A case may also be an unsolved case – a problem to be solved at present or in the futurethe future

Note that some aspects of the law are like this - based on a library of precedents, Note that some aspects of the law are like this - based on a library of precedents, expressed as cases expressed as cases

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CBR History and Current StatusCBR History and Current Status 1980s: Early work done by Roger Schank - Yale University 1980s: Early work done by Roger Schank - Yale University

Funding support from the US Defence Advanced Research Funding support from the US Defence Advanced Research Projects Agency (DARPA)Projects Agency (DARPA)

Started later in Europe - Germany most activeStarted later in Europe - Germany most active

1990: Commercial applications appear1990: Commercial applications appear

1998: Research activity in >35 institutions around the world 1998: Research activity in >35 institutions around the world

15 reported CBR commercial tools 15 reported CBR commercial tools

Many applications in daily useMany applications in daily use

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How CBR worksHow CBR works

The knowledge base of a CBR consists of cases - The knowledge base of a CBR consists of cases - units of experience consisting of problems and units of experience consisting of problems and solutionssolutions

Each case in case base is defined in terms of its Each case in case base is defined in terms of its attributes and the solution found for itattributes and the solution found for it

A Case

Attributes………

Solution…

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Steps in the case-based reasoning Steps in the case-based reasoning cycle:cycle:

1. Retrieve a case matching given problem

2. Adapt the matching case’s solution to produce desired solution

3. Test and revise suggested solution

4. Retain confirmed solution by adding it to case base for future use.

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Steps in the CBR CycleSteps in the CBR CycleProblem

NewCase

Retrieved Case

NewCase

RETRIEVE

Previous Cases

SolvedCase

Tested/RepairedCase

LearnedCase

Case Base

Suggested Solution

REVISE

REUSE

Confirmed Solution

RETAIN

The CBR cycle (Aamodt 1994)

General Knowledge

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A Simple Example of Problem Solving Using CBR (Bergmann 1998)

Problem: Find the cause for a fault in a car and a Problem: Find the cause for a fault in a car and a suggest a repair strategy suggest a repair strategy

Case base consists of cases containing:Case base consists of cases containing: A description of symptomsA description of symptoms A description of the causeA description of the cause A description of the repair strategyA description of the repair strategy

Each case describes one particular diagnostic Each case describes one particular diagnostic situationsituation

It records several features and their specific values It records several features and their specific values found for that situation found for that situation

Each case describes one particular situation Each case describes one particular situation

independently of any anotherindependently of any another

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A Simple Example (continued)A Simple Example (continued)Case 1 Symptoms (Attribute: Value)

Fault : Front lights not turning onCar : VW Golf 1.6LYear : 1993Battery voltage : 13.6 VState of lights : OKState of light switch : OK

SolutionDiagnosis : Front light fuse blownRepair : Replace front light fuse

Case 2 Symptoms (Attribute: Value)Fault : Front lights not turning onCar : Audi A6Year : 1995Battery voltage : 12.9 VState of lights : Surface damagedState of light switch : OK

SolutionDiagnosis : Bulb defectRepair : Replace front light

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A Simple Example (continued)A Simple Example (continued)

To solve a specific fault finding problem A case for it is built up without the solution part Attribute values are gathered by observing symptoms (eg,

engine not starting) and measuring values (eg battery voltage = 6.3V)

Not all attributes values may be known for a problem. In other problem domains the unsolved problem may have

all or part of the solution, but one or more of the attributes missing.

Case to besolved

Symptoms (Attribute: Value)Fault : Brake lights not turning onCar : Audi 80Year : 1989Battery voltage : 12.6 VState of lights : OK

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Similarity computation in simple exampleSimilarity computation in simple example The degree of similarity is expressed using a number in The degree of similarity is expressed using a number in

the range 0.0 (no similarity at all) to 1.0 (complete the range 0.0 (no similarity at all) to 1.0 (complete similarity)similarity)

Front lights not turning on Brake lights not turning on0.8

Front lights not turning on Engine not starting0.4

Symbolic Attribute: Fault

12.6 V 13.6 V0.9

12.6 V 6.7 V0.1

Numeric Attribute: Battery Voltage

AAttributes are given weights reflecting their significance In this example: weight 6 given for higher importance and weight 1 for lower importance

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Adaptation of matching case Adaptation of matching case

in examplein example Once a matching case has been found its solution part is adaptedOnce a matching case has been found its solution part is adapted

The adaptation is based on The adaptation is based on Differences and similarities between the given problem and the matched Differences and similarities between the given problem and the matched

case (Case 1)case (Case 1) How the differences should affect the solutionHow the differences should affect the solution

One heavily weighted attribute is One heavily weighted attribute is faultfault

In the example, fault has values “Brake lights not turning on” and In the example, fault has values “Brake lights not turning on” and “Front lights not turning on”“Front lights not turning on”

Significant similarity in nature (light problem), but difference in Significant similarity in nature (light problem), but difference in specifics (type of light)specifics (type of light)

Differences and similarities in other attributes (eg, make and year of Differences and similarities in other attributes (eg, make and year of manufacture) are not significant manufacture) are not significant

Adaptation done in matching case’s solution part - Adaptation done in matching case’s solution part - “ “front lights” changed to “brake lights”.front lights” changed to “brake lights”.

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Important attribute: weight = 6 Less important attribute: weight = 1

Problem to be solved (Symptoms)- Fault : Brake light not turning on- Car : Audi 80- Year : 1989- Batt. Voltage : 12.6 V- State of lights : OK

Case 1 (Symptoms)- Fault : Front light not turning on- Car : VW Golf- Year : 1993- Batt. Voltage : 13.6 V- State of lights : OK- State of light switch : OK

Solution- Diagnosis : Front light fuse defect- Repair : Replace front light fuse

0.8

0.4

0.6

0.9

1.0

Similarity Computation by Weighted AveragingSimilarity (problem to be solved, case 1) = 1/20 * [6*0.8 = 1*0.4 + 1*0.6 + 6*0.9 +

6*1.0] = 0.86


in examplein example

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Problem to be solved (Symptoms)- Fault : Brake light not turning on- Car : Audi 80- Year : 1989- Batt. Voltage : 12.6 V- State of lights : OK

Case 1 (Symptoms)- Fault : Front light not turning on- …

Solution- Diagnosis : Front light fuse defect- Repair : Replace front light fuse

Adapted Solution- Diagnosis : Brake light fuse defect- Repair : Replace brake light fuse


in examplein example

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Reuse (by adaptation) of the Solution in Case Reuse (by adaptation) of the Solution in Case 11

Case 3 (Symptoms)- Fault : Brake light not turning on- Car : Audi 80- Year : 1989- Batt. Voltage: 12.6 V- State of lights: OK

Solution- Diagnosis : Brake light fuse defect- Repair : Replace brake light fuse

Storage of new Experience

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Representation of CasesRepresentation of Cases

CBR heavily dependent on structure and CBR heavily dependent on structure and content of case basecontent of case base

Case search and retrieval of matching Case search and retrieval of matching processes needs to be efficient processes needs to be efficient

Actual case representation depends on Actual case representation depends on domain and task requirements domain and task requirements

Also influenced by the structure of the Also influenced by the structure of the already available case dataalready available case data

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Some of the representation Some of the representation approachesapproaches

Flat feature-value listFlat feature-value list A simple structure A simple structure Sometimes sufficient for solving problems in a given domainSometimes sufficient for solving problems in a given domain Allows relatively easy storage and retrieval in a CBR system Allows relatively easy storage and retrieval in a CBR system

Object-oriented (OO) representationsObject-oriented (OO) representations Reflect the case structure in a hierarchical fashionReflect the case structure in a hierarchical fashion A case consists of a set of objectsA case consists of a set of objects Objects described by a set of attributesObjects described by a set of attributes Each object belongs to an object-class. Object-classes are Each object belongs to an object-class. Object-classes are

organised in an inheritanceorganised in an inheritance

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Object-oriented (OO) representations (con't)Object-oriented (OO) representations (con't)

Car

Brake system Motor Transmission system

……

Fuel injection systemIgnition system

Spark coil Spark plug

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Graph representationsGraph representations Graph representations consist of Graph representations consist of a set of nodesa set of nodes arcs joining the nodesarcs joining the nodes

A more flexible but complex structure compared with a A more flexible but complex structure compared with a

hierarchical representationhierarchical representation

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Graph representations (con't)Graph representations (con't)

Performance/Cost attributes:Cost < $20 million

Auto

Number of lanes

3

Medium

Concrete

PurposeLength

Deck type

Graph representation of a case (Dhar & Stein 1997)

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Computation of Similarity for MatchingComputation of Similarity for Matching

Different attributes usually carry different levels of significance Different attributes usually carry different levels of significance So simple comparison for retrieving similar cases not useful So simple comparison for retrieving similar cases not useful Attribute values assigned weight values to reflect their significanceAttribute values assigned weight values to reflect their significance This may be done This may be done

A prioriA priori based on user experience based on user experience Depending on importance they assume for a specific problem Depending on importance they assume for a specific problem

instance - “discriminating power”instance - “discriminating power” Attributes may be numeric or symbolicAttributes may be numeric or symbolic

Numeric valued attributesNumeric valued attributes The The nearest-neighbour algorithm nearest-neighbour algorithm is commonly used for measuring is commonly used for measuring

inter-case distancesinter-case distances But the nearest-neighbour algorithm becomes less and less But the nearest-neighbour algorithm becomes less and less

reliable with increasing number of attributesreliable with increasing number of attributes

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Computation of Similarity for MatchingComputation of Similarity for Matching

Symbolic attributesSymbolic attributes Similarity may be measured heuristically by using Similarity may be measured heuristically by using

set/subset relationshipsset/subset relationships Cases represented hierarchically depending upon their Cases represented hierarchically depending upon their

attributesattributes For example, two cases For example, two cases BB and and C C with symbolic with symbolic

attributes x’attributes x’ and and x’’x’’ have the case have the case AA as parent if as parent if x’ x’ and and x’’x’’ are special instances (subsets) of are special instances (subsets) of AA’s attribute ’s attribute xx. .

Distance between two symbolic cases may be Distance between two symbolic cases may be measured by their distances to their common parent. measured by their distances to their common parent.

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Computation of Similarity for Matching Computation of Similarity for Matching

The distance calculation module of a CBR system may take the The distance calculation module of a CBR system may take the form of a statistical model, a rule-based system or a neural form of a statistical model, a rule-based system or a neural network.network.

A

BC

D E

Case D is more similar to case E (common parent B) than to case C (a more distant common parent A)

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Case StorageCase Storage Efficient storage and retrieval of cases is essential for large Efficient storage and retrieval of cases is essential for large

case basescase bases Storage method depends on the case representation scheme, Storage method depends on the case representation scheme,

and the size of the case base.The two main approaches are:and the size of the case base.The two main approaches are:

Linear lists, for small case basesLinear lists, for small case bases Index structures consisting of trees or nets, for large case bases.Index structures consisting of trees or nets, for large case bases.

Internal vs. External StorageInternal vs. External Storage For small case bases and non-shared data, the main memory For small case bases and non-shared data, the main memory

stores the case base.stores the case base. Databases if the case base is large, or if the data is shared with Databases if the case base is large, or if the data is shared with

other applicationsother applications

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Case RetrievalCase Retrieval

Identify Attributes

Initially match

Search & Select

(Partial) Problem Description

Best Matching Case

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Case RetrievalCase RetrievalIdentification of AttributesIdentification of Attributes Generates relevant problem descriptors from the user inputGenerates relevant problem descriptors from the user input Unknown descriptors disregarded or asked to be explained by userUnknown descriptors disregarded or asked to be explained by user Descriptors may be inferred by using contextual general knowledgeDescriptors may be inferred by using contextual general knowledge

Initial MatchInitial Match Cases that match all input attributes are good candidates for selectionCases that match all input attributes are good candidates for selection Cases that match a given fraction of problem features may also be Cases that match a given fraction of problem features may also be

retrieved retrieved Similarity assessment may be more knowledge-intensiveSimilarity assessment may be more knowledge-intensive SelectSelect Best match chosen from good candidates found in initial matchBest match chosen from good candidates found in initial match Involves closer inspection and rankingInvolves closer inspection and ranking Knowledge-intensive selection methods typically generate explanations Knowledge-intensive selection methods typically generate explanations

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Case ReuseCase Reuse

The principal issue for reuses is how to adapt the The principal issue for reuses is how to adapt the solution part from the best matching case to make it solution part from the best matching case to make it suit given problemsuit given problem

Possibilities:Possibilities: No modification of the solution: simply copies matching No modification of the solution: simply copies matching

case’s solution part!case’s solution part! Manual/interactive solution adaptation by the userManual/interactive solution adaptation by the user Automatic solution adaptationAutomatic solution adaptation

Automatic solution adaptation carried out by Automatic solution adaptation carried out by Using the past case solution – known as Using the past case solution – known as transformational transformational

analogyanalogy Using the method that constructed the past case solution – Using the method that constructed the past case solution –

derivational analogyderivational analogy. .

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Case ReuseCase Reuse

In transformational analogy, rules or operators In transformational analogy, rules or operators are used to adjust the past case solution with are used to adjust the past case solution with respect to differences in the two situationsrespect to differences in the two situations

In derivational analogy, the retrieved case In derivational analogy, the retrieved case holds information about the method used for holds information about the method used for solving the retrieved problem. Applies retrieved solving the retrieved problem. Applies retrieved method to the new case. method to the new case.

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Case RevisionCase Revision Consists of two tasks: Consists of two tasks: (1) evaluation of case solution generated by the reuse, and if it fails (1) evaluation of case solution generated by the reuse, and if it fails

evaluation,evaluation, (2) repair of the case solution using domain-specific knowledge(2) repair of the case solution using domain-specific knowledge

The The evaluationevaluation task may take the form of task may take the form of Applying the solution in the real environment to verify its correctness, Applying the solution in the real environment to verify its correctness,

quality & user acceptancequality & user acceptance Computer simulation (try out before you commit)Computer simulation (try out before you commit)

The case repair task involves The case repair task involves detecting errors in the current solution and detecting errors in the current solution and modifying the solution so that failures do not occurmodifying the solution so that failures do not occur

Case retentionCase retention Tested, verified and (if necessary) revised solutions accepted as a correct Tested, verified and (if necessary) revised solutions accepted as a correct

solution solution and retained by adding it to the case baseand retained by adding it to the case base

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Business Applications of CBRBusiness Applications of CBR(Allen 1994)(Allen 1994)

Many focused on case retrieval for decision supportMany focused on case retrieval for decision support Case retrieval avoids the step of case adaptationCase retrieval avoids the step of case adaptation Aid decisions to be based on the most similar available Aid decisions to be based on the most similar available

precedentsprecedents

Customer service help desk Customer service help desk Volatile nature of the problem domainVolatile nature of the problem domain Knowledge acquisition and maintenance too expensive for Knowledge acquisition and maintenance too expensive for

traditional expert systemstraditional expert systems Example - Compaq Computers’ SMART systemExample - Compaq Computers’ SMART system Automation of business processesAutomation of business processes Wide-scale distribution of technical and managerial expertiseWide-scale distribution of technical and managerial expertise

Example - NEC’s SQUAD a corporate-wide system for capture Example - NEC’s SQUAD a corporate-wide system for capture and distribution of software quality control knowledgeand distribution of software quality control knowledge

Some 3000 cases added to the system per year since 1982Some 3000 cases added to the system per year since 1982

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Business Applications of CBRBusiness Applications of CBR

Design and configurationDesign and configuration Support reuse and modification of standard designs Support reuse and modification of standard designs Used by Nippon Steel and Lockheed.Used by Nippon Steel and Lockheed. Applications of CBR systems have also been reported Applications of CBR systems have also been reported

in the following areas:in the following areas: Technical fault diagnosisTechnical fault diagnosis Classification and predictionClassification and prediction Control and MonitoringControl and Monitoring PlanningPlanning Bank loan analysisBank loan analysis

The CBR approach to automating planning and The CBR approach to automating planning and scheduling is an active area of research scheduling is an active area of research

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Phases of CBR DevelopmentPhases of CBR Development

Methodologies for CBR system development share the following Methodologies for CBR system development share the following phases:phases:

Case-base DesignCase-base Design A general representation for cases developed using source A general representation for cases developed using source

materials - written documentation and expert accounts, and materials - written documentation and expert accounts, and database recordsdatabase records

Involves a coordinated effort by user, managers and system Involves a coordinated effort by user, managers and system developersdevelopers

Tasks:Tasks: Compilation of a lexicon of terms used to describe problem attributesCompilation of a lexicon of terms used to describe problem attributes Selection of appropriate attributes for indexing casesSelection of appropriate attributes for indexing cases Specification of database schemas used to store casesSpecification of database schemas used to store cases Definition of case base authoring standards.Definition of case base authoring standards.

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Phases of CBR DevelopmentPhases of CBR Development

Initial Case-Base DevelopmentInitial Case-Base Development A “seed” case base developed as a baselineA “seed” case base developed as a baseline This case base reviewed and refined by users and This case base reviewed and refined by users and

developers until a valid case base covering an developers until a valid case base covering an adequate part of the case space developedadequate part of the case space developed

Ongoing Development and MaintenanceOngoing Development and Maintenance Initial case base further refined through execution of Initial case base further refined through execution of

the revise and retain steps during usethe revise and retain steps during use Case accuracy and utility are monitoredCase accuracy and utility are monitored Case base managed like an organisational database Case base managed like an organisational database

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CBR ToolsCBR Tools Commercial tools like expert system shells, available for building Commercial tools like expert system shells, available for building

CBR systemsCBR systems Facilitate quick development of applicationsFacilitate quick development of applications A typical CBR development environment, as reported in 1994, A typical CBR development environment, as reported in 1994,

provides provides Default database schemes for case representationDefault database schemes for case representation Problem solving tool for case-based decision supportProblem solving tool for case-based decision support Forms used for editing cases, attributes, and solutionsForms used for editing cases, attributes, and solutions Utilities include those for Utilities include those for

manual and automated indexing of casesmanual and automated indexing of cases automatic import of cases from records in relational database tablesautomatic import of cases from records in relational database tables conceptual clustering of cases for analysis.conceptual clustering of cases for analysis.

Some examples of CBR toolsSome examples of CBR tools ReMind from Cognitive Systems Inc.ReMind from Cognitive Systems Inc. CBR Express from Inference CorporationCBR Express from Inference Corporation Esteem from Esteem Software IncEsteem from Esteem Software Inc CasePower from Inductive Solutions Inc.CasePower from Inductive Solutions Inc. ReCall from IsoftReCall from Isoft

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Advantages of CBR systemsAdvantages of CBR systems Solves difficult to model problemsSolves difficult to model problems In many application areas, eg in business, problems are often In many application areas, eg in business, problems are often

unstructured and difficult to modelunstructured and difficult to model Reduced knowledge acquisition effort Reduced knowledge acquisition effort

Reliance on experts modest, especially if good data already availableReliance on experts modest, especially if good data already available Easier for experts to describe case attributes rather than providing Easier for experts to describe case attributes rather than providing

heuristic rules for solutionheuristic rules for solution Reduced maintenance effort Reduced maintenance effort Carried out by addition or deletion of casesCarried out by addition or deletion of cases Reliance on experts is modest, cases easy to understand (cf. Reliance on experts is modest, cases easy to understand (cf.

rules in a rule base)rules in a rule base) High scalability and flexibilityHigh scalability and flexibility Case base easy to expand and refineCase base easy to expand and refine Enhancement and refinement happens as part of the overall Enhancement and refinement happens as part of the overall

operation and useoperation and use Mistakes corrected relatively easily by adapting casesMistakes corrected relatively easily by adapting cases Performance improves over time through refinementPerformance improves over time through refinement Changes in environment get reflected through the addition of new Changes in environment get reflected through the addition of new

and/or deletion of outdated casesand/or deletion of outdated cases

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Weaknesses of CBR systemsWeaknesses of CBR systems Critics of CBR complain that it uses Critics of CBR complain that it uses ad hocad hoc or or

anecdotal evidence as its main operating principle - anecdotal evidence as its main operating principle - weak for the reasons human ad hoc adoption of cases weak for the reasons human ad hoc adoption of cases from memory is weak from memory is weak

Without statistically relevant data for backing and Without statistically relevant data for backing and implicit generalization, there is no guarantee that the implicit generalization, there is no guarantee that the generalization is correctgeneralization is correct

Some of the work in CBR may be 'handed off' to Some of the work in CBR may be 'handed off' to human operator - eg case database needs begin by human operator - eg case database needs begin by hand-crafted cases, which could be difficult to writehand-crafted cases, which could be difficult to write

Response time may suffer as number of cases in the Response time may suffer as number of cases in the case base grows (depends on indexing method)case base grows (depends on indexing method)

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Case Study - Customer Support Case Study - Customer Support (Dhar & Stein 1997, pp.236-243)(Dhar & Stein 1997, pp.236-243)

A very successful intelligent system for customer support system A very successful intelligent system for customer support system developed at Compaq Computer Corporationdeveloped at Compaq Computer Corporation

Given the complexity of a modern personal computer system, Given the complexity of a modern personal computer system, effective customer support is a major undertakingeffective customer support is a major undertaking

The customer support engineer must enter a call for support into a The customer support engineer must enter a call for support into a logging system, analyse the customer’s data, resolve the problem, logging system, analyse the customer’s data, resolve the problem, and deliver the solution.and deliver the solution.

This task is particularly challenging due toThis task is particularly challenging due to Dynamic nature of the problem domain - with an increasing range of Dynamic nature of the problem domain - with an increasing range of

products, support staff faces an increasing variety of questions. products, support staff faces an increasing variety of questions. Widening scope of problem domain - the use of more and more third Widening scope of problem domain - the use of more and more third

party hardware and software that must be integrated with Compaq’s party hardware and software that must be integrated with Compaq’s productsproducts

Distributed expertise - due to the width and diversity of the problem Distributed expertise - due to the width and diversity of the problem domain, few support staff ever experience the full range of problemsdomain, few support staff ever experience the full range of problems

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Case Study (continued)Case Study (continued) Compaq had to either increase its support staff number or reduce the Compaq had to either increase its support staff number or reduce the

number of incoming calls and the time taken to resolve a call, while number of incoming calls and the time taken to resolve a call, while maintaining a high support standardmaintaining a high support standard

Compaq needed to develop an intelligent support system that would be a Compaq needed to develop an intelligent support system that would be a central repository of problem solving expertise.central repository of problem solving expertise.

The requirementsThe requirements The system must integrate knowledge that was highly distributed in The system must integrate knowledge that was highly distributed in

nature. nature. The system must handle a large and changing array of models, products The system must handle a large and changing array of models, products

and configurations.and configurations. Users (service engineers, dealers) must be able to find solutions quickly Users (service engineers, dealers) must be able to find solutions quickly

so that the customer could be got back to within a few minutes of time.so that the customer could be got back to within a few minutes of time. Users need not require deep knowledge about all problem areas.Users need not require deep knowledge about all problem areas. The system must be able to handle incomplete or inexact input since The system must be able to handle incomplete or inexact input since

many customers may not be able to describe the problem fully or many customers may not be able to describe the problem fully or accurately.accurately.

The system must be accessible to many users at different locations.The system must be accessible to many users at different locations. To gain user confidence, some explanation capability was needed.To gain user confidence, some explanation capability was needed. The system must be easily expandable over time to allow new kinds of The system must be easily expandable over time to allow new kinds of

problems to be added.problems to be added.

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Case Study (continued)Case Study (continued)

Some positive aspects of the system development Some positive aspects of the system development problem were:problem were:

Most problems were relatively independent in nature and Most problems were relatively independent in nature and non-interacting with other problems.non-interacting with other problems.

The system did not need to provide an exact diagnosis, just The system did not need to provide an exact diagnosis, just the likely problem area. the likely problem area.

Possible solutionsPossible solutions Apart from a CBR system, there were two other Apart from a CBR system, there were two other

possible solution choices:possible solution choices:

A standard database system - a DBMS would lack A standard database system - a DBMS would lack the rich structures needed for representing the the rich structures needed for representing the problems. Also, such a system would be too rigid to problems. Also, such a system would be too rigid to allow users access to open-ended data.allow users access to open-ended data.

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An expert system - this choice faced the following difficultiesAn expert system - this choice faced the following difficulties The problem domain was not stable enough for experts to express The problem domain was not stable enough for experts to express

solution heuristics with a high degree of confidencesolution heuristics with a high degree of confidence The rule base would require frequent updating due to continuing The rule base would require frequent updating due to continuing

changes in the problem domainchanges in the problem domain Due to the distributed nature of the expertise, the knowledge Due to the distributed nature of the expertise, the knowledge

extraction process would be difficultextraction process would be difficult

A case-based reasoning systemA case-based reasoning system Required enough prototypical cases to cover the problem domainRequired enough prototypical cases to cover the problem domain This had to be done either by using the experts or from data This had to be done either by using the experts or from data

gleaned from customer callsgleaned from customer calls One weakness of a CBR system is the response time as the size of One weakness of a CBR system is the response time as the size of

the case base growsthe case base grows However, it could deal with noisy or partial data, as missing However, it could deal with noisy or partial data, as missing

attributes tend not to affect similarity computation too adverselyattributes tend not to affect similarity computation too adversely

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The solution implementedThe solution implemented The SMART system based on case-based reasoning and integrated The SMART system based on case-based reasoning and integrated

with the call-logging system with the call-logging system The following year Compaq developed a second CBR system called The following year Compaq developed a second CBR system called

QUICKSOURCE, which was also aimed at the customer apart from QUICKSOURCE, which was also aimed at the customer apart from dealers and internal staffdealers and internal staff

Case bases for both systems were built using prototypical cases of Case bases for both systems were built using prototypical cases of previous problems previous problems

The structure of each case was as follows:The structure of each case was as follows: A description of the problem in EnglishA description of the problem in English A set of questions. The answers of the questions could be of the form A set of questions. The answers of the questions could be of the form

yes/no, numeric, or an item from a list. Each question also has a yes/no, numeric, or an item from a list. Each question also has a matchmatch weight and a weight and a mismatchmismatch weight to reflect its importance weight to reflect its importance

A set of actions (the solution part)A set of actions (the solution part)

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System operationSystem operation A customer support staff collects the problem information with a A customer support staff collects the problem information with a

textual descriptiontextual description Simple problems are resolved by him/her straightawaySimple problems are resolved by him/her straightaway Unresolved problems cause the invocation of the CBR system.Unresolved problems cause the invocation of the CBR system. The system performs an initial search for similar cases using The system performs an initial search for similar cases using

keywords from the textual description keywords from the textual description A list of matching cases along with their distance scores is A list of matching cases along with their distance scores is

displayed on the screendisplayed on the screen A list of relevant questions pops up on the screenA list of relevant questions pops up on the screen As the user answers the questions, the list of cases and their As the user answers the questions, the list of cases and their

scores changescores change

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System operation (continued)System operation (continued) If a perfectly matching case is found, the problem is solvedIf a perfectly matching case is found, the problem is solved

If a perfectly matching case is not found, the case is marked If a perfectly matching case is not found, the case is marked “unresolved” and passed on to the case builder experts. It is then “unresolved” and passed on to the case builder experts. It is then reviewed and solvedreviewed and solved

The system gets refined with each new problem solved added to The system gets refined with each new problem solved added to its case baseits case base

To keep the growth of the case base under control, new cases are To keep the growth of the case base under control, new cases are

only added if they are judged to be uniqueonly added if they are judged to be unique

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ResultsResults Some of the benefits achieved by the CBR system were:Some of the benefits achieved by the CBR system were: A larger number of problems could be resolved during the customer’s A larger number of problems could be resolved during the customer’s

interaction with the support system than it was previously possible.interaction with the support system than it was previously possible. The success rate on test cases was 50% higher than that without the CBR The success rate on test cases was 50% higher than that without the CBR

systemsystem Ready access to the case base meant, support staff was more likely to Ready access to the case base meant, support staff was more likely to

search for relevant information more actively instead of passing problems on search for relevant information more actively instead of passing problems on to specialiststo specialists

QUICKSOURCE resulted in 20% fewer calls to Compaq support centre from QUICKSOURCE resulted in 20% fewer calls to Compaq support centre from dealers and customers as only the harder problems got directed to itdealers and customers as only the harder problems got directed to it

The case base served as a valuable repository of product performance The case base served as a valuable repository of product performance information for Compaqinformation for Compaq

The company became less susceptible to departures of experienced support The company became less susceptible to departures of experienced support staffstaff

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REFERENCESREFERENCES

Aamodt, A., & Plaza, E., Aamodt, A., & Plaza, E., Case-Based Reasoning: Foundational Case-Based Reasoning: Foundational Issues, Methodological Variations, and System ApproachesIssues, Methodological Variations, and System Approaches, , AICom – Artificial Intelligence Communications, IOS Press, AICom – Artificial Intelligence Communications, IOS Press, Vol.7:1, 1994, pp.39-59.Vol.7:1, 1994, pp.39-59.

Allen, B. P., Allen, B. P., Case-Based Reasoning Business ApplicationsCase-Based Reasoning Business Applications, , Communications of the ACM, Vol.37, No.3, March 1994, pp.40-42.Communications of the ACM, Vol.37, No.3, March 1994, pp.40-42.

Bergman, R., Bergman, R., Introduction to Case-Based Reasoning¸Introduction to Case-Based Reasoning¸URLURL http://www.cbr-web.org/CBR-Web/cbrintro. http://www.cbr-web.org/CBR-Web/cbrintro.

Dhar, V., & Stein, RDhar, V., & Stein, R.,”Solving Problems by Analogy” in Seven .,”Solving Problems by Analogy” in Seven Methods for Transforming Corporate Data into Business Methods for Transforming Corporate Data into Business IntelligenceIntelligence., Prentice Hall 1997, pp. 149-166, 236-243.., Prentice Hall 1997, pp. 149-166, 236-243.

ICT619 Intelligent Systems Topic 7: Case Based Reasoning

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