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Transcript of CBR for Design Upmanyu Misra CSE 495. Design Research Develop tools to aid human designers Automate...
![Page 1: CBR for Design Upmanyu Misra CSE 495. Design Research Develop tools to aid human designers Automate design tasks Better understanding of design Increase.](https://reader035.fdocuments.us/reader035/viewer/2022070407/56649e4d5503460f94b436b4/html5/thumbnails/1.jpg)
CBR for Design
Upmanyu Misra
CSE 495
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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
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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
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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
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Design Task (figure)
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Approaches for design tasks
Formulae Constraints Rules and grammars CBR Prototype based
reasoning
Routine Only
- Goel (1989), Domeshek and Kolodner (1992), Hinrichs (1992)
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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
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The PCM Model
The CBR Cycle
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Mapping Design Task to CBR-cycle
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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
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A Framework for CBD Systems
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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
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CBR System ArchitectureFour 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
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Case Retrieval for Innovative/Creative design
Flexible case retrieval Structural similarity assessment Similarity assessment in terms of adaptability
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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.
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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”
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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
name
name
name
emptyhalfcase )2(220)1(1 case
name
2(case1,case2) 0.5
emptycase )1(219)2(1 case
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Fish & Shrink
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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
SD(Fx,Fy ,W ) wii(Fx,Fy )
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Fish & Shrink Algorithm
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Fish and Shrink
0
1 Distance to the query
T1
T2
T3
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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
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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
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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:
VVE
)(OP)(OP
)()( PP)( P
)()( PP
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case base_a problem_a
case base_g problem_g
compile compile
retrieve
match adaptSet of cases mcs
recompile
set of solution_a
set of solution_g
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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
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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
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Graph f: Graph g:
a2
b2
b1
a1R4(a,a)
R5(b,b)
R3(a,b)R2(a,b)
R1(b,a)
a4
b4
b3
a3R9(a,b)
R10(b,b)
R8(a,b)R7(b,a)
R6(b,a)
Combination graph
R4(a,a)<=>R9(a,a)
R1(b,a)<=>R7(b,a)
R5(b,b)<=>R10(b,b)
R1(b,a)<=>R6(b,a)
R2(a,b)<=>R8(a,b)
R3(a,b)<=>R8(a,b)
a2
b2
b1
a1R4(a,a)
R5(b,b)
R3(a,b)R2(a,b)
R1(b,a) a4
b4
b3
a3R9(a,b)
R10(b,b)
R8(a,b)R7(b,a)
R6(b,a)
result
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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
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prototype
specifications
concept
prototypeselection
selectionconcept
conceptmodification
conceptoptimization
case base
initial target
retrieve case
retrieve part
structuraladaptation
precedent case
remainingtarget
targetremaining
precedent case&
no
yes
EADOCS Design Process
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Summary
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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”, ???