CBR for Design Upmanyu Misra CSE 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 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

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

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

Fish & Shrink Algorithm

Fish and Shrink 0 1 Distance to the query T1T1 T2T2 T3T3

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:

case base_a problem_a case base_g problem_g compile retrieve matchadapt Set 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 a1 R4(a,a) R5(b,b) R3(a,b) R2(a,b) R1(b,a) a4 b4 b3 a3 R9(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 a1 R4(a,a) R5(b,b) R3(a,b) R2(a,b) R1(b,a) a4 b4 b3 a3 R9(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 prototype selection concept modification concept optimization case base initial target retrieve case retrieve part structural adaptation precedent case remaining target remaining 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”, ???