CBR for Design

1. CBR for Design Upmanyu Misra CSE 495

2. Design Research • Develop tools to aid human designers • Automate design tasks • Better understanding of design • Increase quality • Take lesser time • Improve predictability of design REUSE

3. Reuse of Design • Reuse old design – share intellectual property (IP) • As the ‘reuse’ increases, the complexity increases • Human assistance is mandatory • Directed towards assisting human designer rather than making intelligent decisions by own

4. Design Task • Routine design • is completely a part of a set of potential designs • all variables, their ranges, and knowledge to compute their values are directly derivable from the set • easily implemented • Innovative Design • is partially derived from the set of potential designs • all components need to be derived. The knowledge is incomplete • design needs to be iteratively derived • Creative Design • no overlap with the set of potential design. The set needs to be extended • All components need to be defined

5. Design Task (figure)

6. Approaches for design tasks Routine Only • Formulae • Constraints • Rules and grammars • CBR • Prototype based reasoning - Goel (1989), Domeshek and Kolodner (1992), Hinrichs (1992)

7. The PCM Model • Propose – involves using domain knowledge to map part or all of the specification to partial or complete design proposals • Critique – assessment of the proposed design solution • Modify – takes info about a failure of a proposed design as its input and then changes the design to get closer to the desired specification

8. The PCM Model The CBR Cycle

9. Mapping Design Task to CBR-cycle

10. Case Based Design • Defined as “the process of creating a new design solution by combining and/or adapting previous design solution(s)” • useful tool for intelligent system design in a domain where either an explicit model does not exist or one is not yet adequately understood • can learn from interaction with user

11. A Framework for CBD Systems

12. Characteristics of CBD System • Can produce complete and complex designs based on relatively small knowledge base • design starts from complete cases, implicitly achieving trade-offs between several constraints • design history of existing cases makes design problem solving more efficient • using cases as a source of knowledge allows learning by storing new cases

13. CBR System Architecture Four Knowledge Containers • Vocabulary: should be able to capture all salient features of the design. Task dependent • Case base: • - usage: cases can capture both regular/normal situations as well as exceptions/abnormal situations • - granularity: for task-oriented user support, the grain size of the cases matches that of the decisions made • Similarity measures: to compare queries and cases in their corresponding representations • Solution transformation: contains knowledge required to evaluate solutions

14. Case Retrieval for Innovative/Creative design • Flexible case retrieval • Structural similarity assessment • Similarity assessment in terms of adaptability

15. Case Retrieval • Flexible Case Retrieval – Given a large case base, a problem, and a number of aspects that are relevant for similarity assessment, a set of cases is to be retrieved which show similar aspects as in the actual problem • The aim is to exploit different views on single cases • Importance of certain aspects for similarity assessment may not be known at memory organization time - dynamic weighting is required - use kd trees, Case Retrieval Nets etc.

16. Fish & Shrink Algorithm • Used for Flexible Case Retrieval • Selects and ranks potential cases from a large set of cases • Considers different aspects (representations) of cases • Main idea “it should be more efficient to avoid searching in the nearby neighborhood of cases which have already been found to be inappropriate”

17. Fish & Shrink • A representation function takes the case and outputs the aspect in the desired representation space : case 1: (20, empty, 0.05) case 2: (19, half-empty, 0.9) • A distance function that can take two representations in space and calculate the distance of the two cases in this aspect

18. Fish & Shrink

19. Fish & Shrink Method • View distance SD • The view distance from the query to some case is called test distance, and the view distance between two cases is called the base distance • This is a basic distance function, researchers generally use their own • Presumption: View distance function have to satisfy the triangle equality

20. Fish & Shrink Algorithm

21. Fish and Shrink 0 T3 T1 T2 1 Distance to the query

22. Structural Similarity Assessment and Adaptation Using Graphs • To retrieve structurally most similar cases • Structured case representation → Graph • Find maximum common sub graph • CAD example for industrial building: • Object represented by set of attributes describing its geometry and type • Different pipe system shows different topological relations • Building structure can be mapped onto its pipe system

23. Required Functionality • A compile function is used to translate the selected attributes and their relations into graphs • A recompile function is used to translate the selected solution graphs into their attributes-based representation • Retrieving case is conducted by selecting the case having maximum common sub graph with the problem • Structural adaptation proceeds by combining case parts that are not included in the sub graph

24. Structural similarity assessment and adaptation • A graph g=(V,E), where V is the set of vertex and • mcs(G) is the maximum common sub graph of a set of graphs G • Let be the set of all graphs, O be the a finite set of objects represented by attribute values and other relationships, P( ) be the power set of • compile: • recompile: • retrieve: • match: • adapt:

25. Set of match mcs adapt cases retrieve set of solution_g problem_g case base_g recompile compile compile problem_a case base_a set of solution_a

26. Example of structural similarity assessment: TOPO • Consider geometric neighborhoods as well as structural similarity • Compile and Recompile • There can be several kinds of relations for different types • Retrieval • Use Fish and Shrink algorithm • Search for maximum clique instead of maximum common sub graph • Search clique in one graph representing all possible matches between two graphs, combination graph • Adaptation • Sub graphs that are not in the clique are added to the solution under user defined constraint

27. Combination Graph • Nodes in the combination graph is the matching of relationships in original graph • Two nodes are connected together if the two matching does not contradict each other • clique- finding is done by Bron and Kerbosh’s algorithm, by extending complete sub graph of size k to k+1 by adding vertices connected to all vertices in the found sub graph

28. Graph f: Graph g: b1 b3 R1(b,a) R5(b,b) R6(b,a) R10(b,b) a1 a2 a4 a3 R4(a,a) R9(a,b) R2(a,b) R3(a,b) R7(b,a) R8(a,b) b2 b4 Combination graph result R4(a,a)<=> R9(a,a) b3 R6(b,a) R5(b,b)<=> R10(b,b) R10(b,b) R1(b,a)<=> R7(b,a) b1 a4 a3 R1(b,a) R5(b,b) R9(a,b) a1 a2 R7(b,a) R8(a,b) R4(a,a) b4 R2(a,b)<=> R8(a,b) R3(a,b) R1(b,a)<=> R6(b,a) R2(a,b) b2 R3(a,b)<=> R8(a,b)

29. Structural Adaptation by Case Combination • Example: EADOC, supporting conceptual design phase in designing aircraft panel structure • User specifies initial requirements, objectives, and preferences • Specific plans for certain model is not available • Partial model for evaluating behavior are available • Four CBR cycles, each results in a set of solution that can serve as the input for next cycle • Additional information is needed to guide the retrieval • Solutions can be biased to previous tasks

30. EADOCS Design Process specifications prototype selection initial target prototype remaining target retrieve part & precedent case concept retrieve case selection case base yes no concept remaining structural target adaptation concept precedent case modification concept optimization

31. Summary

32. References • JÄorg Walter Schaaf, “Fish & Shrink. A next step towards efficient case retrieval in large scaled case bases”, EWCBR’96 • Ian Watson, Srinath Parera, “Case-Based Design: A Review and Analysis of Building Design Applications, Journal of AI for Engineering Design, Analysis and Manufacturing”, 1997 • Katy Borner, “CBR for Design”, ???