Case-Based Reasoning P R I N C I P L E S & P R A C T I C E CBRCBR.

Case-Based Reasoning

P R I N C I P L E S & P R A C T I C ECBR

Outline

• An Introduction to Case-Based Reasoning

Standard CBR Model

• Research & Applications

Limitations & Extensions

• The Future ...

Introducing Case-Based Reasoning

• Motivations

• The Standard CBR Model

• A Case Study

• The Story So Far ...

Motivating CBR

• Regularity

The world is a regular place - similar problems have similar solutions.

• Repetition

The world is a repetitive place - similar problems tend to recur.

• Availability of Cases

The Standard CBR Model

Target Problem

Case-Base

Retrieval

AdaptationLearning

Property Valuation: A Case Study

Type:

Location:

Bedrooms:

Rcpt Rooms:

Grounds:

Age:

Condition:

PRICE:

Bungalow

Co. Wicklow

3

2

1/3 Acre

New

Excellent

? Solution

• Rule-based Approach?

Correct & Consistent Rules?

Type:Location:Bedrooms:Rcpt Rooms:Grounds:Age:Condition:

BungalowCo. Wicklow321/3 AcreNewExcellent

Target Problem

Type:Location:Bedrooms:Rcpt Rooms:Grounds:Age:Condition:Price:

BungalowCo. Wicklow321/4 Acre5 YearsExcellent£85,000

Case

Simple Similarity

Count the matching features to compute a score...

Retrieving Similar Cases

Target Problem

85%

70%

65%

50%40% 85%

Similar Cases

Select the best matching case (highest score) ...

Adapting the Best Case


Price:

BungalowCo. Wicklow321/3 AcreNewExcellent

£100,000

Target Problem


Price:

BungalowCo. Wicklow321/4 Acre5 YearsExcellent

£85,000

Case

Price + £10k

Price + £5k

Modify the case’s price to account for mismatches...

Potential Advantages

• Problem Solving Efficiency

Reuse vs First-Principles

• Knowledge Engineering Effort

Acquiring & Maintaining Cases

• User Acceptance

Embedded Systems vs Case-Based Assistants

Application Areas

• Classification & Prediction

Credit Card Fraud Detection, Property Valuation

• Diagnosis & Decision Support

Help-Desk Support, Fault Diagnosis, Air Traffic Control

• Planning & Design

Automatic Software Design, Route Planning, Scheduling

The Story So Far ...

• Simplified CBR

Single-Shot CBR

Simple Retrieval & Adaptation

• Limitations

Representing Complex Cases

Sophisticated Models of Similarity

Learning Cases & Adaptation Knowledge

Single-Shot CBR

• Limitations

Complete problem descriptions are needed for retrieval.

Complex problems may be more readily solve by reusing and combining (parts of) many cases.

• Solutions

Incremental Case-Based Reasoning (ICBR)

Hierarchical Case-Based Reasoning (HCBR)

Incremental CBR

• Motivations

Incomplete Problem Descriptions (Eg, Help-Desks, Diagnosis)

Feature Costs (Potentially many expensive tests or questions)

• Solution

Skeletal cases used to initiate retrieval

Early remindings guide the elicitation of extra information

Example: Help-Desk Support

Problem: Paper Jam

What sort of paper are you using?

Problem: Paper JamPaper : Envelopes :Solution:Glueless Envelopes

Case 1

Paper: Slides

Problem: Paper JamPaper : Slides :Solution:Heat Res. Slides

Right. If the slides aren’t heat resistant

they will jam.

Case 2

ICBR Advantages

• Diagnostic Features are Economically Selected

Information theory ensures the selection of information-rich features in order to optimise diagnostic costs.

Irrelevant features are ignored and expensive tests may be avoided.

• Assistant Technologies

ICBR offers a ideal interactive framework for CBR assistants.

ICBR & Circuit Diagnosis

• Microprocessor Fault Diagnosis

Large number of potential features.

Varying costs due to the nature of features tests.

A given diagnosis may depend on a relatively small number of features.

Cases readily available.

• Results

30% - 90% reduction in feature tests.

Hierarchical CBR

• Motivations

Complex problems require complex solutions.

Retrieving and adapting a single case is unlikely to prove viable.

• Solution

Decompose complex problems into simpler units.

Retrieve, adapt, and combine cases.

Deja Vu: Software Design

• Plant-Control Software

Steel Production Robots (Unloading/Loading Coils of Steel)

Complex Control Programs

• Hierarchical Structure

Programs can be decomposed into simpler units and recombined to produce complex solutions.

Case Hierarchies

Problem A Problem B

Abstract CaseConcrete Case

• Individually reusable abstract & concrete cases

Common sub-problems can be shared thereby improving the storage efficiency of the case-base.

Retrieval Issues

• Key Issue

When is a case similar to the target problem?

• Problems

Assessing relative feature importance.

The relationship between similarity & adaptation.

The Weighting Game

• “Location, location, location…”

Relative feature important can be critical in assessing case similarity. Eg, the location feature in property valuation.

Importance encoded as feature weights.

Similarity(T,C)=w1.Sim(ft1,fc

1)+…+wn.Sim(ftn,fc

n)

Feature Weights

CaseSimilarity

Feature Similarity

Assigning & Adjusting Weights

• Hand Coded

Time Consuming - Another Knowledge Acquisition Bottleneck?

Error Prone - Weights can be context sensitive.

• Automatic Learning Techniques

Weights adjusted by analysing problem solving successes and/or failures.

Success => Increase weights of matching features.

Failure => Decrease weights of matching features.

Push & Pull

Adjust feature weights to reduce similarity between target and incorrect case, thereby pushing the incorrect case away from the target.

Case A(Incorrect)

Target

Case B

(Correct)

Adjust feature weights to increase similarity between target and correct case, thereby pulling the correct case towards the target

Example: Air Traffic Control

Conflict Resolution Problem

Select Aircraft

Select Manoeuvre

Crash Course!

Example: Air Traffic Control

• Conflict Resolution in ATC

Case-Base of past conflicts plus resolutions.

• Complex Feature Weights

Important features difficult to determine.

Learning technique improved retrieval performance from 61% to 81%.

Similarity vs Adaptability

• The Similarity Assumption

Cases, similar to the target, are easy to adapt.

This assumption is often wrong!

• Solution

Adaptability should be measured during retrieval.

Retrieve adaptable cases. How?

Adaptation Guided Retrieval

• Adaptation Knowledge Guides Retrieval

Knowledge about what can and cannot be adapted easily is used to validate matches and mismatches during retrieval.

Retrieval Space Adaptation Space

AdaptationRetrieval

Adaptation Knowledge

Example: Deja Vu

• Plant-Control Software Design

Surface similarities between features often disguise underlying adaptation problems.

• Results

Improved retrieval accuracy.

Improved system performance.

Adaptation

• Rule-Based Adaptation

Adaptation expertise encoded as a set of rules.

Knowledge acquisition problems.

• Solution

Automatically learn adaptation rules.

How?

Adaptation-Rule Induction


BungalowCo. Wicklow321/3 AcreNewExcellent£100,000


BungalowCo. Wicklow321/4 AcreNewExcellent£85,000

IF Grounds: 1/4 Acre > 1/3 Acre THEN +£15,000

Adaptation Rule

Adaptation-Rule Induction

• Constrain Comparisons

Limiting Case Comparisons

• Pruning Generated Rules

Merging Rules

Generalisation

• Results

Viable Adaptation Knowledge

Learning in CBR

• Learning Feature Weights

• Learning Adaptation Knowledge

• Learning New Cases

Newly solved problems = new cases!

Expertise accumulates as more and more problems are solved.

Learning Issues

• Conventional Wisdom

“More cases is a good thing”

• The Utility Problem

Excess cases can cause performance problems as case retrieval eventually becomes prohibitively expensive.

Saturation Point

Coping Strategies

• Case Forgetting

Delete cases which do not contribute to system performance in a positive way.

• Implications

Competence Problems

Case-Base SizeE

ffic

ien

cy

Saturation Point

Optimal system efficiency

Future Work

• Case-Base Maintenance

• Distributed CBR

• Future Applications

Case-Base Maintenance

• Need for Maintenance

Large-scale, Dynamic Case-Bases

Out-of-Date Cases

Incorrect/Inconsistent Cases

Performance Tuning

• Techniques

Feature Weight & Adaptation Knowledge Learning

Automatic Case Deletion

Distributed CBR

• CBR-Net

Web-based CBR Systems (Help Systems, Online Shopping)

• Issues

Distributed Client/Server Case-Bases

Distributed Retrieval

Adaptive Maintenance

Future Applications

• Personalised Content Delivery

• Product Selection

• Personalised Virtual Worlds

Personalised Virtual Worlds

• VRML on the Web

3D interactive worlds.

Automatically construct worlds to suit the needs of individual users.

Eg., Personalised shopping malls.

Conclusions

• Case-Based Reasoning

“Reasoning as Remembering”

• Application Areas

Prediction/Classification, Diagnosis, Planning, Design

• Future Work...

Case-Based Reasoning P R I N C I P L E S & P R A C T I C E CBRCBR.

Documents

Transcript of Case-Based Reasoning P R I N C I P L E S & P R A C T I C E CBRCBR.