Case Based Reasoning

1. Case Based Reasoning Advanced Knowledge Based Systems Module CM4023 Susan Craw Room SAS B18a s.craw@comp.rgu.ac.uk http://www.comp.rgu.ac.uk/staff/smc/teaching/kbp3/

2. How do we solve problems? • By knowing the steps to apply • from symptoms to a plausible diagnosis • But not always applying causal knowledge • diseases cause symptoms • symptoms do not cause diseases! • How does an expert solve problems? • uses same “book learning” as a novice • but quickly selects the right knowledge to apply • Heuristic knowledge (“rules of thumb”) • “I don’t know why this works but it does and so I’ll use it again!” • difficult to elicit © The Robert Gordon University, Aberdeen

3. Another way we solve problems? • By remembering how we solved a similar problem in the past • This is Case Based Reasoning (CBR)! • memory-based problem-solving • re-using past experiences • Experts often find it easier to relate stories about past cases than to formulate rules © The Robert Gordon University, Aberdeen

4. Problems we solve this way • Medicine • doctor remembers previous patients especially for rare combinations of symptoms • Law • English/US law depends on precedence • case histories are consulted • Management • decisions are often based on past rulings • Financial • performance is predicted by past results © The Robert Gordon University, Aberdeen

5. Solution CBR Solving Problems Review Retain Database Adapt Retrieve Similar New Problem © The Robert Gordon University, Aberdeen

6. CBR System Components • Case-base • database of previous cases (experience) • episodic memory • Retrieval of relevant cases • index for cases in library • matching most similar case(s) • retrieving the solution(s) from these case(s) • Adaptation of solution • alter the retrieved solution(s) to reflect differences between new case and retrieved case(s) © The Robert Gordon University, Aberdeen

7. R4 Cycle RETRIEVE find similar problems RETAIN integrate in case-base CBR REUSE propose solutions from retrieved cases REVISE adapt and repair proposed solution © The Robert Gordon University, Aberdeen

8. Failure prediction ultrasonic NDT of rails for Dutch railways water in oil wells for Schlumberger Failure analysis Mercedes cars for DaimlerChrysler semiconductors at National Semiconductor Maintenance scheduling Boeing 737 engines TGV trains for SNCF Planning mission planning for US navy route planning for DaimlerChrysler cars Applications © The Robert Gordon University, Aberdeen

9. e-Commerce sales support for standard products sales support for customised products Personalisation TV listings from Changing Worlds music on demand from Kirch Media news stories via car radios for DaimlerBenz Re-Design gas taps for Copreci Formulation (recipes) rubber for racing tyres for Pirelli tablets for AstraZeneca More Applications © The Robert Gordon University, Aberdeen

10. provides bulk to be large enough to handle and compress (~65%) makes it cohesive to hold together filler binder enables it to come out of the die allows rapid break down after swallowing lubricant disintegrant drug aids wetting and dissolution of drug active ingredient (~25%) surfactant What’s in a Tablet? © The Robert Gordon University, Aberdeen

11. Tablet Formulation Problem • Given: • physical and chemical properties of a drug • desired dose • Knowing: • properties of available excipients • Goal: • choose 5 excipients and their quantities • which achieve the desired mechanical and chemical properties of the tablet Solution filler DCP 92.3% binder GEL 2.1% lubricant MGS 1.0% disintegrant CRO 2.1% surfactant SLS 0.3% © The Robert Gordon University, Aberdeen

12. Get-Insoluble-Filler IF: Reqd-Filler-Solubility has value Insoluble Filler is-on Filler-Agenda Solubility has value Sol in Filler Slightly-Soluble has value Slightly-Soluble Sol < Min-Val (Slightly-Soluble) THEN refine Filler to be Filler in Formulation Remove-Excessive-Fillers IF: Filler is-on Filler-Agenda Max-Level of Filler is Level Filler-Concentration has value Conc Conc > Level THEN ... Drug Properties Excipient Properties Drug/Excipient Stabilities Chemical Relationships Physical Relationships Heuristics Try to balance physical properties with stable excipients to achieve a tablet with viable properties Tablet Formulation Knowledge © The Robert Gordon University, Aberdeen

13. Solution CBR for Tablet Formulation Review Retain Database Adapt formulations for existing tablets soluble drug? => insoluble filler larger dose? => less filler Retrieve Similar Dose & Properties of New Drug tablets of similar dose whose drugs have similar properties © The Robert Gordon University, Aberdeen

14. P P P P P P P P P S S S S S S S S S CBR Assumption • New problem can be solved by • retrieving similar problems • adapting retrieved solutions • Similar problems have similar solutions ? X © The Robert Gordon University, Aberdeen

15. Why was filler X chosen? • The tablet in the case-base whose • drug properties are most similar • dose is most similar is Drug-Y-50 and its filler is Z • However adaptation is needed • because of a significant difference • the stability of Z with the new drug is much lower • Adaptation proposes filler Xinstead: • greater stability with new drug • similar properties to Z © The Robert Gordon University, Aberdeen

16. CBR Knowledge Containers • Cases • lesson to be learned • context in which lesson applies • Description Language • features and values of problem/solution • Retrieval Knowledge • featuresused to indexcases • relative importance of features used for similarity • Adaptation Knowledge • circumstances when adaptation is needed • alteration to apply © The Robert Gordon University, Aberdeen

17. Corporate Memory • Cases from database, archive, . . . • Issues • case bias? currency? coverage? • description language e.g. agreement on terms • Case-base cannot contain allformulations • good coverage • prototypicaland exceptional cases • Opportunity for multiple sources • several expert formulators • shared knowledge across companies © The Robert Gordon University, Aberdeen

18. Case Representation • feature-value representation • Problem • drug properties and dose • Solution • excipients and their amounts • Extra tablet properties • constrained features of resulting tablet © The Robert Gordon University, Aberdeen

19. Database Relevant Cases Most Similar Cases Vote C4.5 Index progress of retrieval K Nearest Neighbour Similarity Matching Gshadg hjshfd fhdjf hjkdhfs hjdshfl hfdjsfhdjs hjdhfl hsdfhl hd hdjsh hjsdkh hfds hhfkfd shk Gshadg hjshfd fhdjf hjkdhfs hjdshfl hfdjsfhdjs hjdhfl hsdfhl hd hdjsh hjsdkh hfds hhfkfd shk Tcl for adaptation CBR Tool © The Robert Gordon University, Aberdeen

20. Nearest Neighbour Retrieval • Retrieve most similar • k-nearest neighbour • k-NN • like scoring in bowls or curling • Example • 1-NN • 5-NN © The Robert Gordon University, Aberdeen

21. How do we measure similarity? • Distances between values of individual features • problem and case have values p and c for feature f • Numeric features • f(problem,case) = |p - c|/(max difference) • Symbolic features • f(problem,case) = 0 if p = c = 1 otherwise • Distance is (problem,case) • weighted sum of f(problem,case) for all features • Similarity(problem, case) = 1/(1+ (problem,case)) © The Robert Gordon University, Aberdeen

22. ? ? 300 200 100 0 High Low Decision Trees as an Index Solubility? low high ? Dose? <200 >200 ? © The Robert Gordon University, Aberdeen

23. Case Retrieval • Typical implementation • e.g. Case-Base indexedusing a decision-tree Cases are “stored” in the index leaves… • from these the most similar are retrieved using similarity matching © The Robert Gordon University, Aberdeen

24. 300 200 100 0 High Low Why do we want an index? • Efficiency • if similarity matching is computationallyexpensive • Pre-selection of relevant cases • some features of new problem may make certain cases irrelevant . . . • despite being very similar © The Robert Gordon University, Aberdeen

25. Case Retrieval Parameters • Selection of features • inducing decision tree index • Weights for features • similarity matching • Parameters to induce • decision tree index • Number of best-matches • retrieved by similarity measure © The Robert Gordon University, Aberdeen

26. Are CBR Systems Easy to Develop? Review Retain Database Adapt Adaptation Knowledge Retrieve Past Cases Similar Not Necessarily! OK? Similarity Knowledge © The Robert Gordon University, Aberdeen

27. Case-base Similarity Matching Index Adaptation CBR System Profiles CBRA Adaptation rules Acquiring Knowledge Database of previous formulations © The Robert Gordon University, Aberdeen

28. Learning • Case-base • inserting new cases into case-base • updating contents of case-base to avoid mistakes • Retrieval Knowledge • indexing knowledge • features used • new indexing knowledge • similarity knowledge • weighting • new similarity knowledge • Adaptation knowledge © The Robert Gordon University, Aberdeen

29. CBR Resources • CBR Tools • ReCall (www.isoft.fr), Orenge (www.tecinno.com) Kaidara (www.kaidarausa.com) • CBR Websites • www.ai-cbr.org • www.aic.nrl.navy.mil/~aha/ • www.scms.rgu.ac.uk/research/kbs/kacbd/ • CBR Conferences • ICCBR’01: www.iccbr.org/iccbr01/ • UK-CBR’01: www.ai-cbr.org/ukcbr5/ • ECCBR 2002: www.scms.rgu.ac.uk/eccbr2002/ © The Robert Gordon University, Aberdeen

30. Reading • Useful texts • (Kolodner 1993, Aamodt & Plaza 1994, Thompson 1997) • Our papers • Case-Based Design for Tablet Formulation. Craw, Wiratunga & Rowe. Proc. 4th European Workshop on CBR, p358-369, Springer, 1998. • Self-Optimising CBR Retrieval. Jarmulak, Craw & Rowe. Proc 12th Int Conf on Tools with AI. IEEE Press, 2000. • Using Case-Base Data to Learn Adaptation Knowledge for Design. Jarmulak, Craw & Rowe. Proc 17th Int Joint Conf on AI. AAAI Press, 2001. • Also see http://www.scms.rgu.ac.uk/research/kbs/kacbd/ © The Robert Gordon University, Aberdeen

31. CBR vs Rule-based KBS • Rule-based • a rule is generalised experience • applies to range of examples • currently do not learn as they solve problems • knowledge acquisition bottleneck • Case-based reasoning • cases include both prototypical cases and exceptions • indexing,similarity and adaptation control effectiveness • domain does not have an effective underlying theory • learning updates case-base • knowledge acquisition? • retrieval and adaptation knowledge © The Robert Gordon University, Aberdeen

32. Pros & Cons of CBR • Advantages • solutions are quickly proposed • derivation from scratch is avoided • domains do not need to be completely understood • cases useful for open-ended/ill-defined concepts • highlights important features • Disadvantages • old cases may be poor • library may be biased • most appropriate cases may not be retrieved • retrieval/adaptation knowledge still needed © The Robert Gordon University, Aberdeen

33. Summary • CBR Cycle • retrieve, reuse, revise, retain • Knowledge containers • case-base and description language • retrieval and adaptation knowledge • CBR tools to ease development of CBR systems • C4.5 index and k-NN retrieval • adaptation? • Knowledge acquisition • case knowledge can be easy • retrieval/adaptation knowledge may not be easy © The Robert Gordon University, Aberdeen