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Case-based Reasoning A type of analogical reasoning

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Title: Case-based Reasoning A type of analogical reasoning


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 calledSimilarity 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 2012 - European Conference on Case-Based
    Reasoning
  • ICCBR 2013 - 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

14
How to Use a Case
Problem of the case
Problem
Solution ?
Solution of the case
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 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 U
pdate 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

repair
Observations
diagnosis
classification
Domain rules
Diagnosis occurs frequently in after sales
support
19
An Example OverviewTypical Scenario Call Center
  • Technical Diagnosis of Car Faults
  • symptoms are observed (e.g., engine doesnt
    start) and values are measured (e.g., battery
    voltage 6.3V)
  • goal Find the cause for the failure (e.g.,
    battery empty) anda 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 doesnt 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

CASE1
21
A Case Base With Two Cases
  • Problem (Symptoms)
  • Problem Front light doesnt 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

CASE1
  • Each case describes one particular situation
  • All cases are independent of each other
  • Problem (Symptoms)
  • Problem Front light doesnt 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

CASE2
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

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
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
Case 1 (Symptoms) Problem Front light doesnt
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) Problem Front light
doesnt work Car Audi A4 Year 1997 Battery
voltage 12,9 V State of lights surface
damaged State of light switch OK Solution Diagnos
is Bulb defect Repair Replace front light
New Problem Case Problem Break light doesnt
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 doesnt work
Break light doesnt work
0.4
Front light doesnt work
Engine doesnt start
0.9
13.6 V
12.6 V
0.1
6.7 V
12.6 V
26
Compare Similarity
  • Problem (Symptom)
  • Problem Break light doesnt work
  • Car Audi 80
  • Year 1989
  • Battery voltage 12.6 V
  • State of lights OK
  • Problem (Symptoms)
  • Problem Front light doesnt 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
0
Very important feature weight 6
Less important feature weight 1
  • Similarity computation by weighted average
  • similarity(new,case 2) 1/20 60.8 10.8
    10.4 60.95 60 0.585
  • Case 1 is more similar due to feature State
    of lights

27
Compare Similarity
  • Problem (Symptom)
  • Problem Break light doesnt work
  • Car Audi 80
  • Year 1989
  • Battery voltage 12.6 V
  • State of lights OK
  • Problem (Symptoms)
  • Problem Front light doesnt 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 60.8 10.4
    10.6 60.9 6 1.0 0.86

28
Case adaption algorithm
CASE1
  • Problem (Symptoms)
  • Problem Front light doesnt work
  • ...
  • Solution
  • Diagnosis Front light fuse defect
  • Repair Replace front 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 doesnt 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

CASE3
30
The Classical CBR R4-Cycle
This cycle shows the main activities in CBR
from Aamodt Plaza, 1994
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 U
pdate 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 lt a lt b lt 1 with the intention
  • If sim(newproblem, caseproblem) lt a then the case
    is not accepted
  • If sim(newproblem, caseproblem) gt 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). 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 4
Approximation (exact matching vs approx matching) 1 1 4 4 4
Adaptable 4 2 4 4 2
Learnable 3 1 4 4 2
Interpretable 3 4 1 2 4
Learns from scratch 4 1 4 1 3
42
Case Based Reasoning Algorithm
43
Case Based Forecasting
44
Case Base in Java
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