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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. Index. CBR process The framework for CBR

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A framework for case-based reasoning in engineering design

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  1. 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

  2. Index • CBR process • The framework for CBR • Organization of CBR model • Architecture of CASETOOL NTUST Ailab Li-we Pan

  3. 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 NTUST Ailab Li-we Pan

  4. 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. NTUST Ailab Li-we Pan

  5. Solution transformation • 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 NTUST Ailab Li-we Pan

  6. An engineering design solution can be represented in the form hierarchical decomposition of components • Arranging the relevant parts of cases to be taken up in the order of preference. • Solution transformation at the subgoal level NTUST Ailab Li-we Pan

  7. Case storing • 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 NTUST Ailab Li-we Pan

  8. Organization of CBR model • 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 NTUST Ailab Li-we Pan

  9. Problem data 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) NTUST Ailab Li-we Pan

  10. RN ( Relevance Norm ) • 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) NTUST Ailab Li-we Pan

  11. A 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 NTUST Ailab Li-we Pan

  12. Example of CBR • 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 NTUST Ailab Li-we Pan

  13. Match RN Value perfect 0.0-1/3 of RNmax close 1/3-1.0 of RNmax NTUST Ailab Li-we Pan

  14. Linguistic Class Critic Rating 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 NTUST Ailab Li-we Pan

  15. Component Cases(in the order of preference) 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 NTUST Ailab Li-we Pan

  16. CBR model Current Problem Data Retrieved Cases Solution TRANSFORMER RETRIEVER Condition causing Failures Case-Base ANTICIPATOR STORER NTUST Ailab Li-we Pan

  17. CASE-BASED REASONING TOOL-KIT (CASETOOL) 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) NTUST Ailab Li-we Pan

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