CBR for Design

Upmanyu MisraCSE 495

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

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

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

Design Task (figure)

Approaches for design tasks Formulae Constraints Rules and grammars CBR Prototype based

reasoning

Routine Only

- Goel (1989), Domeshek and Kolodner (1992), Hinrichs (1992)

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

The PCM Model

The CBR Cycle

Mapping Design Task to CBR-cycle

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

A Framework for CBD Systems

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

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

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

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.

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”

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

Fish & Shrink

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 )

Fish & Shrink Algorithm

Fish and Shrink

0

1 Distance to the query

T1

T2

T3

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

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

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

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

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

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

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

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

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

Summary

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”, ???