case based reasoning
TRANSCRIPT
![Page 1: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/1.jpg)
Case Based Reasoning
PKB - Antonie
![Page 2: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/2.jpg)
Faced this situation before?
• Oops the car stopped. – What could have gone wrong?
• Aah.. Last time it happened, there was no petrol. – Is there petrol?
• Yes.
– Oh but wait I remember the tyre was punctured (ban bocor)
• This is the normal thought process of a human when faced with a problem which is similar to a problem he/she had faced before.
![Page 3: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/3.jpg)
How do we solve problems?
• By knowing the steps to apply – from symptoms/gejala to a plausible diagnosis
• But not always applying causal knowledge
– sebab - akibat
• 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
![Page 4: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/4.jpg)
So what?
• Reuse the solution experience when faced with a similar problem.
• 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
![Page 5: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/5.jpg)
What’s CBR?
• To solve a new problem by remembering a previous similar situation and by reusing information and knowledge of that situation
• Ex: Medicine– doctor remembers previous patients especially for rare
combinations of symptoms
• Ex: Law– English/US law depends on precedence– case histories are consulted
• Ex: Management– decisions are often based on past rulings
• Ex: Financial– performance is predicted by past results
![Page 6: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/6.jpg)
Definitions of CBR
• Case-based reasoning is […] reasoning by remembering - Leake, 1996
• A case-based reasoner solves new problems by adapting solutions that were used to solve old problems - Riesbeck & Schank, 1989
• Case-based reasoning is a recent approach to problem solving and learning […] - Aamodt & Plaza, 1994
![Page 7: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/7.jpg)
History
• Roots of CBR is found in the works of Roger Shank on dynamic memory.
• Other trails into the CBR field has come from– Analogical reasoning
– Problem solving and experimental learning within philosophy and psychology
• The first CBR system, CYRUS developed by Janet Kolodner at Yale university.
![Page 8: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/8.jpg)
The Limitations of Rules
• The success of rule-based expert systems is due to several factors:– They can mimic some human problem-solving
strategies– Rules are a part of everyday life, so people can relate
to them
• However, a significant limitation is the knowledge elicitation bottleneck– Experts may be unable to articulate their expertise
• Heuristic knowledge is particularly difficult
– Experts may be too busy…
![Page 9: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/9.jpg)
CBR Cycle
![Page 10: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/10.jpg)
R4 Cycle
REUSEREUSEpropose solutions from retrieved cases
REVISEREVISEadapt and repair
proposed solution
CBRCBR
RETAINRETAINintegrate in
case-base
RETRIEVERETRIEVEfind similar problems
![Page 11: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/11.jpg)
CBR System Components
• Case-base – database of previous cases (experience)
• 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)
![Page 12: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/12.jpg)
CBR Assumption(s)• The main assumption is that:
– Similar problems have similar solutions: • e.g., an aspirin can be taken for any mild pain
• Two other assumptions:– The world is a regular place: what holds true
today will probably hold true tomorrow • (e.g., if you have a headache, you take aspirin,
because it has always helped)
– Situations repeat: if they do not, there is no point in remembering them
• (e.g., it helps to remember how you found a parking space near that restaurant)
![Page 13: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/13.jpg)
Two big tasks of CBR
• Classification tasks (good for CBR)– Diagnosis - what type of fault is this?
– Prediction / estimation - what happened when we saw this pattern before?
• Synthesis tasks (harder for CBR)– Engineering Design– Planning
– Scheduling
![Page 14: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/14.jpg)
Technical Diagnosis of Car Faults
![Page 15: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/15.jpg)
Case Representation
• Flat feature-value list
• Object Oriented representation
• Graph representation
• The choice of representation is – Dependent on requirements of domain and
task– Structure of already available case data
![Page 16: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/16.jpg)
Problem to be solved
![Page 17: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/17.jpg)
How CBR solves problems
• New problem can be solved by– retrieving similar problems– adapting retrieved solutions
• Similar problems have similar solutions
?
SSS
SS S
SS S
PP
PPPP
P
PP
X
![Page 18: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/18.jpg)
CBR Knowledge Containers• Cases
– lesson to be learned– context in which lesson applies
• Description Language– features and values of problem/solution
• Retrieval Knowledge– features used to index cases– relative importance of features used for similarity
• Adaptation Knowledge– circumstances when adaptation is needed– alteration to apply
![Page 19: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/19.jpg)
Corporate Memory• Cases from database, archive, . . .
• Issues– case bias? coverage?– description language e.g. agreement on terms
• Case-base cannot contain all formulations– good coverage– prototypical and exceptional cases
• Opportunity for multiple sources– shared knowledge across companies
![Page 20: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/20.jpg)
New Car Diagnosis Problem
• A new problem is a case without a solution part
• Not all problem features must be known– same for cases– Problem
• Symptom: brakelight does not work• Car: Ford Fiesta
• Year: 1997
• Battery: 9.2v
• Headlights: undamaged• HeadlightSwitch: ?
Feature Value
New
![Page 21: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/21.jpg)
• Compare new problem to each case• Select most similar
• Similarity is most important concept in CBR– When are two cases similar?– How are cases ranked according to similarity?
• Similarity of cases – Similarity for each feature
• Depends on feature values
Retrieving A Car Diagnosis Case
New Problem Case
Ca
se
Case
Ca
se
Case
Ca
se
Case
Ca
se 1Similar?
![Page 22: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/22.jpg)
Similarity Computation for case 1
Figure Credit: R. Bergmann, University of Kaiserslautern
![Page 23: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/23.jpg)
Similarity Computation for case 2
Figure Credit: R. Bergmann, University of Kaiserslautern
![Page 24: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/24.jpg)
Similarity Measurement
• Purpose: To select the most relevant case• Basic Assumption: Similar problems have similar
solutions• Similarity value between 0 and 1 are assigned
for feature value pairs• E.g.: Feature: ProblemFront Light does not work
Break Light does not work .8
Front Light does not work
Engine doesn’t start.4
![Page 25: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/25.jpg)
Similarity Measurement
• Feature: Battery Voltage
• Different features have different importance
• Two kinds of Similarity Measures– Local Similarity – similarity on feature level– Global Similarity - similarity on case or object
level
12.6 13.6 12.6 6.7.9 .1
![Page 26: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/26.jpg)
Calculating Feature Similarity• Distances between values of individual features
– problem and case have values p and c for feature f
– Distance for Numeric features
• df(problem,case) = |p - c|/(max difference)
– Distance for Symbolic features
• df(problem,case) = 0 if p = c = 1 otherwise
• Similarityf(problem,case) = 1 - d
• Degree of similarity is between 0 and 1
![Page 27: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/27.jpg)
Reuse Solution from Case 1
New Problem• Symptom: brakelight does not work
• Car: Ford Fiesta• Year: 1997
• Battery: 9.2v
• Headlights: undamaged• HeadlightSwitch: ?
Problem• Symptom: headlight does not work• …
Solution
• Diagnosis: headlight fuse blown• Repair: replace headlight fuse
– Solution to New Problem• Diagnosis: headlight fuse blown• Repair: replace headlight fuse
– After Adaptation• Diagnosis: brakelight fuse blown• Repair: replace brakelight fuse
Case 1
![Page 28: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/28.jpg)
Matching strings
• exact match: two strings are similar if they are spelled the same way
• spelling check: compares the number of letters which are the same in two strings (Useful for strings consisting of one word only)
• word-count: counts the number of matching words of two cases. (Useful for strings consisting of several words).
![Page 29: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/29.jpg)
Indexing: Why do we want an index?
• Efficiency– if similarity matching
is computationally expensive
• Relevancy of cases for similarity matching
• Cases are pre-selected from case-base
High Low
200
0
100
300
![Page 30: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/30.jpg)
What to index?
Client Ref #: 64Client Name: John SmithAddress: 39 Union StreetTel: 01224 665544Photo:
Age: 37Occupation: IT AnalystIncome: £ 20000…
Unindexed features
Indexed features
Case Features are:- Indexed - Unindexed
![Page 31: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/31.jpg)
High Low
200
0
100
300
Decision Trees as an Index
Solubility?
Dose??
?
?
?
low high
<200 >200
![Page 32: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/32.jpg)
Re-Using Retrieved Solutions
• Single retrieved solution– Re-use this solution
• Multiple retrieved solutions– Vote/average of retrieved solutions
• Weighted according to– Ranking– Similarity
• Iterative retrieval– Solve components of the solution one at a
time
![Page 33: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/33.jpg)
How to Adapt the Solution
• Adaptation alters proposed solution:• Null adaptation - copy retrieved solution
– Used by CBR-Lite systems
• Manual or interactive adaptation– User adapts the retrieved solution (Adapting is easier
than solving?)
• Automated adaptation– CBR system is able to adapt the retrieved solution
– Adaptation knowledge required
![Page 34: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/34.jpg)
Automated Adaptation Methods
• Substitution– change some part(s) of the retrieved solution– simplest and most common form of adaptation
• Transformation– alters the structure of the solution
• Generative– replays the method of deriving the retrieved solution
on the new problem– most complex form of adaptation
![Page 35: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/35.jpg)
Examples of Adaptation
• CHEF – CBR system to plan Szechuan recipes
• Hammond (1990)
• Substitution adaptation– substitute ingredients in the retrieved recipe to
match the menu• Retrieved recipe contains beef and broccoli• New menu requires chicken and snowpeas• Replace chicken for beef, snowpeas for broccoli
• Transformation adaptation – Add, change or remove steps in the recipe
• Skinning step added for chicken, not done for beef
![Page 36: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/36.jpg)
Examples of Adaptation
• Car diagnosis example– Symptoms, faults and repairs for brake lights
are analogous to those for headlight– Substitution: brake light fuse
• Planning example– Train journeys and flights are analogous– Transformation: flights need check-in step
added
![Page 37: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/37.jpg)
Retention
• What can be learned– New experience to be retained as new case– Representing the new case
• Contents of new case• Indexing of new case
• Forgetting cases– For efficiency or because out of date
– Deleting an old case• Old is not necessarily bad• Does it leave a gap?
![Page 38: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/38.jpg)
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
![Page 39: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/39.jpg)
CBR Tool
C4.5 Index
K Nearest NeighbourSimilarityMatching
prog
ress
of r
etrie
val
Database
Relevant Cases
Most SimilarCases
Vote
Tcl for adaptation
Gshadg hjshfdfhdjf hjkdhfs hjdshfl
hfdjsfhdjs hjdhfl hsdfhlhd hdjsh hjsdkh hfds hhfkfd shkGshadg hjshfd
fhdjf hjkdhfs hjdshflhfdjsfhdjs hjdhfl hsdfhl
hd hdjsh hjsdkh hfds hhfkfd shk
![Page 40: Case based reasoning](https://reader033.vdocuments.net/reader033/viewer/2022052909/5597d9e21a28abc45e8b46fc/html5/thumbnails/40.jpg)
CBR Resources
• Books– I. Watson. Applying Knowledge Management: Techniques For
Building Corporate Memories. Morgan Kaufmann, 2003.– I. Watson. Applying Case-Based Reasoning: Techniques for
Enterprise Systems. Morgan Kaufmann, 1997.• CBR on the web
– http://groups.yahoo.com/group/case-based-reasoning/ • CBR Commercial Solutions
– Orenge from www.empolis.com– Kaidara Adviser from (www.kaidara.com)– eGain (www.egain.com)
• Customer Service & Contact Centre Software
• CBR Tools in our School– CBR-Works from www.empolis.com– ReCall from www.isoft.fr– Weka from www.cs.waikato.ac.nz