1. Case-based Reasoning A type of analogical reasoning

2. Problem Solving Humans are extremely good at problem solving
They have evolved that way
Even problems that they seldom encounter
or have never encountered before

3. Problem Solving Logics
Deductive Logic
Inductive Logic
Abductive Logic
Analogical Logic
Description Logic
Fuzzy Logic

4. Analogy and Experience

5. Underlying Model of CBR
Humans are robust problem solvers
Humans reason from cases in a wide variety of contexts
Studies abound in how humans reason from cases

6. What is CBR? Case-based reasoning is [...] reasoning by remembering.
A case-based reasoner solves new problems by adapting solutions that were used to solve old problems.
Case-based reasoning is a recent approach to problem solving and learning
Case-based reasoning models both the ways people use cases to solve problems and the ways in which we can make machines use cases.

7. Analogical Reasoning (CBR)
Case-based reasoning (CBR) is a certain technique which was based on analogical reasoning.
The main intention is to reuse previous experiences for actual problems.
The difficulty arises when the actual situation is not identical to the previous one: There is an inexactness involved.
Its main aspect is that CBR-techniques allow inexact (approximate) reasoning in a controlled manner.
Here we will shortly describe its main features.
Major applications include fault diagnosis, help desk systems, eCommerce

8. Could be called Similarity Based Reasoning
The central notion in CBR is the concept of similarity.
The methods in CBR have been extended in a way which allows applications to other problems rather than reusing previous experiences:
in electronic commerce e.g. to product selection.
This is due to an abstract formulation of the similarity concept.
In particular, the main algorithms of CBR can still be applied to these new situations.
We will first describe the original technique informally and then proceed to the extensions.

9. Research in CBR
ECCBR European Conference on Case-Based Reasoning
ICCBR International Conference on Case based Reasoning

10. Case-Based Reasoning (CBR)
Basic Ideas:
Store previous experience (case)
Solve new Problems by selecting and reusing cases
Store new experience again
Replaces 0-1-logic by approximation
Is a well-founded technology:
Mathematically
Algorithmically
With respect to software technology
Supported by experiments and applications
Business success
Most successful recent branch of AI

11. What is a Case ? A case has two parts:
Description of a problem or a set of problems (generalized case)
Description of the solution of this problem (formally or informally)
Possibly additions like explanations, comments on the quality of the solution etc.
Cases represent experiences : They record how a problem was solved in the past

12. Different Case Representations
Free text: textual CBR
Database like representations: structural CBR

13. Structured Case Representation
Many different case representations are used Depend on requirements of domain and task
Structure of already available case data
Flat feature-value list
Simple case structure is sometimes sufficient for problem solving
Easy to store and retrieve in a CBR system
Object-oriented representations
Case: collection of objects (instances of classes)
Required for complex and structured objects
Genauer im Teil III.

14. How to Use a Case Problem of the case Problem Solution ? Solution
Solution adaptation
In general, there is no guarantee for getting good solutions
because the case may be „too far away“ from the problem. Therefore the problem arises how to define when a case is “good enough”.

15. How to Use a Case-Base A case base is a data base of cases
If a new problem arises one will use a case from the case base in order to solve the problem
If we have many cases then the chance is higher to find one with a suitable solution
Because the given problem is usually not exactly in the base one wants to retrieve a case which solved a problem which is „similar enough to be useful“
Hence, the notion of similarity is central to CBR
The concept of similarity based retrieval is compared with data base retrieval

16. Components of CBR In order to solve problems one needs knowledge.
Where is it located: In knowledge containers.
Solution
trans-
formation
Case
base
Similarity measure
Vocabulary
Storage
Compilation
Cases have not
to be understood in
order to be stored
Data
Information
Knowledge
A task of knowledge management is the
maintenance of the containers.

17. The Classical CBR Algorithm
This cycle shows the main
activities in CBR
Retrieve
Reuse
Revise
Retain
Case
Base
Knowledge
New
Retrieved
Solved
Learned
Tested/
Repaired
Suggested
Solution
Problem
Confirmed
Learning
Adaptation
Similarity
Verification
Retrieve:
Determine most similar case(s).
Reuse:
Solve the new problem re-using information and knowledge in the retrieved case(s).
Revise:
Evaluate the applicability of the proposed solution in the real-world.
Retain:
Update case base with new learned case for future problem solving.

18. Typical Problems Handled with CBR: Classification and Diagnosis
A class is a certain subset of some universe and a classification assigns to each element one or more classes to which it belongs.
In fault diagnosis the classification is only the first step:
Observations
diagnosis
repair
classification
Domain rules
Diagnosis occurs frequently in after sales support

19. An Example Overview Typical Scenario: Call Center
Technical Diagnosis of Car Faults:
symptoms are observed (e.g., engine doesn’t start) and values are measured (e.g., battery voltage = 6.3V)
goal: Find the cause for the failure (e.g., battery empty) and a repair strategy (e.g., charge battery)
Case-Based Diagnosis:
a case describes a diagnostic situation and contains:
description of the symptoms
description of the failure and the cause
description of a repair strategy
store a collection of cases in a case base
find case similar to current problem and reuse repair strategy

20. A Simple Example (II) What does a Case Look Like?
A case describes one particular diagnostic situation
A case records several features and their specific values occurred in that situation
 A case is not a ( general) rule !!
Feature
Value
Problem (Symptoms)
Problem: Front light doesn’t work
Car: VW Golf IV, 1.6 l
Year: 1998
Battery voltage: 13,6 V
State of lights: OK
State of light switch: OK
Solution
Diagnosis: Front light fuse defect
Repair: Replace front light fuse
C A S E 1

21. A Case Base With Two Cases
Problem (Symptoms)
Problem: Front light doesn’t work
Car: VW Golf III, 1.6 l
Year: 1996
Battery voltage: 13,6 V
State of lights: OK
State of light switch: OK
Solution
Diagnosis: Front light fuse defect
Repair: Replace front light fuse
C A S E 1
Each case describes one particular situation
All cases are independent of each other
Problem (Symptoms)
Problem: Front light doesn’t work
Car: Audi A4
Year: 1997
Battery voltage: 12,9 V
State of lights: surface damaged
State of light switch: OK
Solution
Diagnosis: Bulb defect
Repair: Replace front light
C A S E 2

22. Solving a New Diagnostic Problem
A new problem has to be solved
We make several observations in the current situation
Observations define a new problem
Not all feature values have to be known
Note: The new problem is a “case” without solution part
Feature
Value
Problem (Symptom):
Problem: Break light doesn’t work
Car: Audi 80
Year: 1989
Battery voltage: 12.6 V
State of Lights: Surface damaged
State of light switch: OK

23. You are required to identify between case 1 and case 2 the case that is most similar to to the problem case
Compare the New Problem with Each Case and Select the Most Similar Case :
C A S E x
Similar?
New Problem
When are two cases similar?
How to rank the cases according to their similarity?
 Similarity is the most important concept in CBR !!
We can assess similarity based on the similarity of each feature
Similarity of each feature depends on the feature value.
BUT: Importance of different features may be different

24. Class Exercise Which case is most similar to the problem case?
Case 1 (Symptoms)
Problem: Front light doesn’t work
Car: VW Golf III, 1.6 l
Year: 1996
Battery voltage: 13,6 V
State of lights: OK
State of light switch: OK
Solution
Diagnosis: Front light fuse defect
Repair: Replace front light fuse
Case 2 (Symptoms)
Car: Audi A4
Year: 1997
Battery voltage: 12,9 V
State of lights: surface damaged
Diagnosis: Bulb defect
Repair: Replace front light
New Problem Case
Problem: Break light doesn’t work
Car: Audi 80
Year: 1989
Battery voltage: 12.6 V
State of Lights: Surface damaged
State of light switch: OK
Which case is most similar to the problem case?

25. A similarity algorithm
Not similar
Very similar
Assignment of similarities for features values.
Express degree of similarity by a real number between 0 and 1
Examples:
Feature: Problem
Feature: Battery voltage (similarity depends on the difference)
Different features have different importance (weights)!
High importance: Problem, Battery voltage, State of light, ...
Low importance: Car, Year, ...
0.8
Front light doesn’t work
Break light doesn’t work
0.4
Front light doesn’t work
Engine doesn’t start
0.9
12.6 V
13.6 V
0.1
12.6 V
6.7 V

26. Compare Similarity Similarity computation by weighted average
Problem (Symptom)
Problem: Break light doesn’t work
Car: Audi 80
Year: 1989
Battery voltage: 12.6 V
State of lights: OK
Problem (Symptoms)
Problem: Front light doesn’t work
Car: Audi A4
Year: 1997
Battery voltage: 12.9 V
State of lights: surface damaged
State of light switch: OK
Solution
Diagnosis: Front light fuse defect
Repair: Replace front light fuse
0.8
0.8
0.4
0.95
Very important feature: weight = 6
Less important feature: weight = 1
Similarity computation by weighted average
similarity(new,case 2) = 1/20 * [ 6* * * * *0 ] = 0.585
Case 1 is more similar: due to feature “State of lights”

27. Compare Similarity Similarity computation by weighted average
Problem (Symptom)
Problem: Break light doesn’t work
Car: Audi 80
Year: 1989
Battery voltage: 12.6 V
State of lights: OK
Problem (Symptoms)
Problem: Front light doesn’t work
Car: VW Golf III, 1.6 l
Year: 1996
Battery voltage: 13.6 V
State of lights: OK
State of light switch: OK
Solution
Diagnosis: Front light fuse defect
Repair: Replace front light fuse
0.8
0.4
0.6
0.9
1.0
Very important feature: weight = 6
Less important feature: weight = 1
Similarity computation by weighted average
similarity(new,case 1) = 1/20 * [ 6* * * * * 1.0 ] = 0.86

28. Case adaption algorithm
Problem (Symptoms):
Problem: Front light doesn’t work
...
Solution:
Diagnosis: Front light fuse defect
Repair: Replace front light fuse
Adapt Solution:
How do differences in the problem affect the solution?
Problem (Symptom):
Problem: Break light doesn’t work
Car: Audi 80
Year: 1989
Battery voltage: 12,6 V
State of light: OK
New Solution:
Diagnosis: Break light fuse defect
Repair: Replace break light fuse

29. New case inserted into the case library ??
If diagnosis is correct:
Store new case in the memory.
Problem (Symptoms):
Problem: Break light doesn’t work
Car: Audi 80
Year: 1989
Battery voltage: 12.6 V
State of lights: OK
State of light switch: OK
Solution:
Diagnosis: break light fuse defect
Repair: replace break light fuse
C A S E 3

30. The Classical CBR R4-Cycle
This cycle shows the main
activities in CBR
[from: Aamodt & Plaza, 1994]
Retrieve
Reuse
Revise
Retain
Case
Base
Knowledge
New
Retrieved
Solved
Learned
Tested/
Repaired
Suggested
Solution
Problem
Confirmed
Learning
Adaptation
Similarity
Verification
Retrieve:
Determine most similar case(s).
Reuse:
Solve the new problem re-using information and knowledge in the retrieved case(s).
Revise:
Evaluate the applicability of the proposed solution in the real-world.
Retain:
Update case base with new learned case for future problem solving.

31. Retrieve: Modeling Similarity
The similarity based retrieval realizes an inexact match which is still useful:
Useful solutions from a case base
Useful products from a product base
Different approaches depending on case representation
Similarity measures:
Are functions to compare two cases sim: Case x Case ® [0..1]
Local similarity measure: similarity on feature level
Global similarity measure: similarity on case or object level

32. Similarities (1)
Similarities are described by measures with numerical values
They operate on
problem descriptions, demands, products ,...
Intention:
The more similar two problem descriptions C and D are,
the more useful it is two use one of the solutions also
for the other problem.
The more similar a demand and a product are the more
useful is the product for satisfying the demand.

33. Similarities and Inexact Reasoning
The similarity measure controls the utility when inexact solutions are employed or the desired product is not exactly as desired available.

34. A Typical Similarity Measure
Given two problem descriptions C1, C2
p attributes y1, ..., yp used for the representation
simj : similarity for attribute yj (local measure)
wj : describes the relevance of attribute j for the problem

35. Nearest Neighbor
Problem: Should a person be granted a load or not (Ian Watson Slide)
Depends on Monthly income and loan amount.
The loan decisions will be clustered

36. Retrieval: Finding The Nearest Neighbor
For a new problem C the nearest neighbor in the case base is the case (D,L) for which problem D has the greatest similarity to C.
Its solution L is intended to be most useful and is then the best solution the case base can offer (or best available product).
Classical databases use always total similarity (i.e. equality).
The access to data in databases is in similarity based systems replaced by the search for the nearest neighbor. It can be regarded as an optimization process.
This requires more effort but can be much more useful.

37. Thresholds
The nearest neighbor (in the given case base) is not always sufficient for providing an acceptable solution.
On the other hand, a case which is not the nearest neighbor may be sufficient enough.
For this purpose one can introduce two thresholds a and b, 0 < a < b < 1 with the intention
If sim(newproblem, caseproblem) < a then the case is not accepted;
If sim(newproblem, caseproblem) > b then the case is accepted.
This partitions this case base (for the actual problem into three parts: accepted cases, unaccepted cases and an uncertainty set. The same works for product bases.

38. Retrieve: Efficiency Issues
Efficient case retrieval is essential for large case bases and large product spaces.
Different approaches depending
on the representation
complexity of similarity computation
size of the base
Organization of the base:
Linear lists, only for small bases
Index structures for large bases, e.g., kd-trees,
How to store cases or products:
Databases: for large bases or if shared with other applications
Main memory: for small bases, not shared

39. Reuse: How to Adapt the Solution
No modification of the solution: just copy. Manual/interactive solution adaptation by the user.
Automatic solution adaptation :
Transformational Analogy: transformation of the solution
Rules or operators to adjust solution w.r.t. differences in the problems
Knowledge required about the impact of differences
Compositional adaptation: combine several cases to a single solution

40. Summary CBR is a technique for solving problems based on experience
CBR problem solving involves four phases:
Retrieve, Reuse, Revise, Retain
CBR systems store knowledge in four containers:
Vocabulary, Case Base,
Similarity Concept, Solution Adaptation
Large variety of techniques for:
representing the knowledge, in particular, the cases
realizing the different phases
CBR has several advantages over traditional KBS
The basic techniques of CBR can be extended to the needs of E-Commerce.
Some Slides adapted from:
Dr. Michael Richter and Dr. Ralph Bergman, University of Kaiserslautern
and University of Calgary AI Resources

41. Table 1 (Adapted from [Abr, Jac] and [Neg])
Table 1 (Adapted from [Abr, Jac] and [Neg]). A comparison of the utility of case based reasoning systems (CBR), rule induction systems (RI), neural networks (NN) genetic algorithms (GA) and fuzzy systems (FS), with 1 representing low and 4 representing a high utility.
CBR
(Experience)
KBS NN GA FL
Know. rep. 3 4 1 2
Approximation (exact matching vs approx matching)
Adaptable
Learnable
Interpretable
Learns from scratch

42. Case Based Reasoning Algorithm
Retrieve
Reuse
Revise
Retain
Case
Base
Knowledge
New
Retrieved
Solved
Learned
Tested/
Repaired
Suggested
Solution
Problem
Confirmed
Learning
Adaptation
Similarity
Verification

43. Case Based Forecasting

44. Case Base in Java