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

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: Bungalow Co. Wicklow 3 2 1/3 Acre New Excellent Target Problem Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/4 Acre 5 Years Excellent £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 Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/3 Acre New Excellent £100,000 Target Problem Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/4 Acre 5 Years Excellent £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 Jam Paper : Envelopes : Solution:Glueless Envelopes Case 1 Paper: Slides Problem: Paper Jam Paper : 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 Case Concrete 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)=w 1.Sim(f t 1,f c 1 )+…+w n.Sim(f t n,f c n ) Feature Weights Case Similarity 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 SpaceAdaptation 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 Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/3 Acre New Excellent £100,000 Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/4 Acre New Excellent £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 Size Efficiency 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

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...