Case-Based Reasoning

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

2. Outline • An Introduction to Case-Based Reasoning • Standard CBR Model • Research & Applications • Limitations & Extensions • The Future ...

3. Introducing Case-Based Reasoning • Motivations • The Standard CBR Model • A Case Study • The Story So Far ...

4. 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

5. Retrieval Case-Base The Standard CBR Model Target Problem Learning Adaptation

6. Property Valuation: A Case Study • Rule-based Approach? • Correct & Consistent Rules? Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: PRICE: Bungalow Co. Wicklow 3 2 1/3 Acre New Excellent ? Solution

7. Simple Similarity Count the matching features to compute a score... Target Problem Case Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/4 Acre 5 Years Excellent £85,000 Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Bungalow Co. Wicklow 3 2 1/3 Acre New Excellent

8. 70% 65% 40% 85% 85% 50% Retrieving Similar Cases Select the best matching case (highest score) ... Similar Cases Target Problem

9. Adapting the Best Case Modify the case’s price to account for mismatches... Case Target Problem Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/4 Acre 5 Years Excellent £85,000 Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/3 Acre New Excellent £100,000 Price + £5k Price + £10k

10. Potential Advantages • Problem Solving Efficiency • Reuse vs First-Principles • Knowledge Engineering Effort • Acquiring & Maintaining Cases • User Acceptance • Embedded Systems vs Case-Based Assistants

11. 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

12. The Story So Far ... • Simplified CBR • Single-Shot CBR • Simple Retrieval & Adaptation • Limitations • Representing Complex Cases • Sophisticated Models of Similarity • Learning Cases & Adaptation Knowledge

13. 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)

14. 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

15. Example: Help-Desk Support Case 1 Problem: Paper Jam Paper : Envelopes : Solution:Glueless Envelopes Problem: Paper Jam Paper: Slides Case 2 Problem: Paper Jam Paper : Slides : Solution:Heat Res. Slides What sort of paper are you using? Right. If the slides aren’t heat resistant they will jam.

16. 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.

17. 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.

18. 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.

19. 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.

20. Case Hierarchies • Individually reusable abstract & concrete cases • Common sub-problems can be shared thereby improving the storage efficiency of the case-base. Problem A Problem B Abstract Case Concrete Case

21. Retrieval Issues • Key Issue • When is a case similar to the target problem? • Problems • Assessing relative feature importance. • The relationship between similarity & adaptation.

22. 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,fc1)+…+wn.Sim(ftn,fcn) Case Similarity Feature Similarity Feature Weights

23. 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.

24. Push & Pull Adjust feature weights to reduce similarity between target and incorrect case, thereby pushing the incorrect case away from the target. Adjust feature weights to increase similarity between target and correct case, thereby pulling the correct case towards the target Case A (Incorrect) Case B (Correct) Target

25. Example: Air Traffic Control Crash Course! Conflict Resolution Problem Select Aircraft Select Manoeuvre

26. 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%.

27. 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?

28. 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 Adaptation Adaptation Knowledge Retrieval Space Adaptation Space

29. Example: Deja Vu • Plant-Control Software Design • Surface similarities between features often disguise underlying adaptation problems. • Results • Improved retrieval accuracy. • Improved system performance.

30. Adaptation • Rule-Based Adaptation • Adaptation expertise encoded as a set of rules. • Knowledge acquisition problems. • Solution • Automatically learn adaptation rules. • How?

31. Adaptation-Rule Induction Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/4 Acre New Excellent £85,000 Type: Location: Bedrooms: Rcpt Rooms: Grounds: Age: Condition: Price: Bungalow Co. Wicklow 3 2 1/3 Acre New Excellent £100,000 Adaptation Rule IF Grounds: 1/4 Acre > 1/3 Acre THEN +£15,000

32. Adaptation-Rule Induction • Constrain Comparisons • Limiting Case Comparisons • Pruning Generated Rules • Merging Rules • Generalisation • Results • Viable Adaptation Knowledge

33. 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.

34. 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

35. Saturation Point Optimal system efficiency Efficiency Case-Base Size Coping Strategies • Case Forgetting • Delete cases which do not contribute to system performance in a positive way. • Implications • Competence Problems

36. Future Work • Case-Base Maintenance • Distributed CBR • Future Applications

37. 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

38. Distributed CBR • CBR-Net • Web-based CBR Systems (Help Systems, Online Shopping) • Issues • Distributed Client/Server Case-Bases • Distributed Retrieval • Adaptive Maintenance

39. Future Applications • Personalised Content Delivery • Product Selection • Personalised Virtual Worlds

40. Personalised Virtual Worlds • VRML on the Web • 3D interactive worlds. • Automatically construct worlds to suit the needs of individual users. • Eg., Personalised shopping malls.

41. Conclusions • Case-Based Reasoning • “Reasoning as Remembering” • Application Areas • Prediction/Classification, Diagnosis, Planning, Design • Future Work...