A framework for case based reasoning in engineering design
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A framework for case-based reasoning in engineering design. H.Shiva Kumar and C.S. Krishnamoorthy, Artificial Intelligence for Engineering Design, Analysis and Manufacturing 1995. 指導老師 : 何正信教授 學生:潘立偉 學號: M8702048 日期: 87/11/28. Index. CBR process The framework for CBR

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A framework for case based reasoning in engineering design
A framework for case-based reasoning in engineering design

H.Shiva Kumar and C.S. Krishnamoorthy,

Artificial Intelligence for Engineering Design, Analysis and Manufacturing 1995

指導老師 : 何正信教授

學生:潘立偉

學號:M8702048

日期:87/11/28

NTUST Ailab Li-we Pan


Index
Index

  • CBR process

  • The framework for CBR

  • Organization of CBR model

  • Architecture of CASETOOL

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Cbr process
CBR process

  • Case retrieval

    • Proper indexing if the cases is of critical importance for selecting the relevant

    • Indexing the cases point

      • Indices must be truly relevant

      • Indices must be generalized to enable selection of all the closely fitting cases

      • Indices should not be over-generalized to include very loosely fitting cases

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  • Retrieval based on the qualitative and quantitative similarities of past cases.

  • Determination of the extent of similarities based on the actual deviations in the values of governing attributes.

  • Determination of past performance of past critical evaluations in order to anticipate the potential of failure of each solution.

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  • Solution transformation similarities of past cases.

    • Adapting the old cases to suit the requirements of new situations.

    • Focusing on the appropriate portions of the case

    • Deriving an appropriate decision on the new case

      • Direct solution transfer

      • Solution transfer with modifications

      • Solution building using the same methods adopted in a similar previous case

      • Schema-based solution transfer

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  • Case storing form hierarchical decomposition of components

    • the new cases are examined to determine whether the new case is worth storing as a design case.

    • These are given below

      • case should help solving some distinct problem.

      • Differences shouldn’t be too great as this gives rise to more modifications and repairs whiles solving new problem.

      • Differences should not be too small as it increases the size of the case-based enormously

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Organization of cbr model
Organization of CBR model form hierarchical decomposition of components

  • Case retrieval

    • Selection :

      • selection a set of cases after weeding out all the loosely connected cases that are chosen based on index.

    • Relevance

      • classifying cased based on the degree of similarity between a given situation and the selected cases.

    • Performance

      • classifying cases based on past performance

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Problem data form hierarchical decomposition of components

Case index

Choosing cases bases based on index

Search conditions

Weed out loosely connected cases

(selection)

Classification based on deviations

(relevance)

Classification based on critic ratings

(performance)

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  • RN ( Relevance Norm ) form hierarchical decomposition of components

  • vi : the value n properties(attributes used in case retrieval)

  • ai : the corresponding properties of the given current situation

  • wi : the relative importance factors

  • The cases are classified as perfect(approximately exact) and close(partial)

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A form hierarchical decomposition of components

part of

part of

part of

part of

B

C

D

E

part of

part of

a

b

c

F

d

e

f

g

G

h

i

j

k

l

m

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Example of cbr
Example of CBR form hierarchical decomposition of components

  • The sample design artifact A

  • components(B, C, …,G)

  • 13 attributes(a,b,…m)

  • p, q, r, s, and t : governing

  • wp,wq,wr,ws,and wt : weightages

  • p, q : qualitative & r, s, t : quantitative

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Match RN Value form hierarchical decomposition of components

perfect 0.0-1/3 of RNmax

close 1/3-1.0 of RNmax

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Linguistic Class Critic Rating form hierarchical decomposition of components

very good 75-100

good 50-75

average 30-50

bad 0-30

Case Relevance Performance

case1 perfect very good

case2 close good

case3 perfect average

case4 close good

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Component Cases(in the order of preference) form hierarchical decomposition of components

1st 2nd 3rd 4th

B case1 case4 case2 case3

F case1 case4 case2 case3

C case1 case4 case2 case3

D case1 case4 case2 case3

G case1 case2 case4 case3

E case1 case2 case4 case3

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CBR model form hierarchical decomposition of components

Current Problem Data

Retrieved Cases

Solution

TRANSFORMER

RETRIEVER

Condition causing Failures

Case-Base

ANTICIPATOR

STORER

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CASE-BASED REASONING TOOL-KIT (CASETOOL) form hierarchical decomposition of components

Organization of DEKBASE

DATA BASE MANAGEMENT SYSTEM (DBMS)

EXP ERT

RULE BASE INFERENCE ENGINE (RBIE)

FRAME MANAGEMENT SYSTEM

(FMS)

USER

ENGINEERING DESIGN SYNTHESIZER (EDS)

GENERIC CRITIQUING TOOL

(GENCRIT)

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