Design for IDSS

Liam Page

CSE 435

23 October 2006

What is design?

Construction of an artifact from single parts that may be either known and given or newly created for this particular effect (Börner 1998)

Design systems assist a user in producing better designs in shorter amount of time

What is design?

How does design help with:

decreasing design times?

increasing design quality?

improving design predictability?

Classifying Design Task

Three classifications:

Routine Design

Innovative Design

Creative Design

Routine Design

State space is well defined using potential designs

New designs can be derived entirely from existing designs

Outcomes known before hand Final design agrees with configurable

constraints Used mostly in KB-systems

Innovative Design

Well defined state space of potential designs, non-routine design desired

Values for variables may change Solution is similar to old designs, but

also appears to be new due to variables

Complex Design

Non-routine design New variables

Extends/moves state space of potential designs

Complex and Innovative Tasks (1)

Often unsure what the final design constraints will be

Typically ordered in accordance to preference criteria

Abstract -> Concrete Reduction of design space

Complex and Innovative Tasks (2)

Ideal system Assists user, not automated User interface logically constructed for

type of design task Learns from past solutions and user’s

response to solutions (accept, correct, refuse)

Case Based Design

Themes of case based designed systems (Maher and Gomez de Silva Garza 1997) representation and management of

complex cases case augmentation using generalized

design knowledge formalization of informal knowledge

Case Based Design

What can be a complex case? Sample of larger data model Data represented structurally (graphs) Non-static variables Flexible – may have multiple

interpretations Adaptable to solve new problems

Case Based Design

Implications of complex cases Must be able to reinterpret and reformulate new

problems Overlapping of problem and past cases must be

identified Parts must be chosen for transfer and

combination Similarity functions must be flexible Joint consideration of case aspects is possible

Example of Complex Case Usage

Case: DeluxeBathroom1

• Dimensions = (20’-40’)x (20’-40’)

• Doors = 1 – 2

• Outlets = 4 – 6

• Hot tub = yes

• …

Case: DeluxeBathroom2

• Dimensions = ( 30’ 50’)x (30’x50’)

• Doors = 2 – 3

• Outlets – 6 – 10

• Deluxe Standing Shower = yes

• …

Transformed Solution

• Dimensions = 30’ x 30’

• Doors = 1

• Outlets = 6

• Deluxe Standing Shower = yes

• …

Case Based Design

Generalized design knowledge to augment cases Includes causal models, state

interactions, heuristic models/rules, geometric constraints

Typically not available for innovative and creative tasks

Case Based Design

Need formalization of knowledge for CBR automation

Problem: human knowledge of design is difficult to formalize into rules and variables that the system can utilize

In cases where it is only possible to create an informal body of knowledge, system should be developed to merely support a human designer

Knowledge Representation

Four knowledge containers in CBR Vocabulary Case base Similarity measure Solution transformation

Vocabulary

Vocabulary – task and domain dependent Should capture all important features of

design Supports problem solving in relevant

domain

Case Base

Represent past design experience Usage – abnormal/normal Granularity – grain size of cases is equal to

grain size of design task Level of Abstraction

Ossified cases – general rules of thumb Paradigmatic cases – represent learned

expertise Stories – complex, relate to large number of

circumstance

Case Base (cont)

Perspective State-oriented – case represents problem

and solution Solution-path – case refer to problem or

operator that determines solution from problem description

Similarity Measure

Two different approaches to similarity assessment Computational (similarity) approach Representational approach

Computational Approach

Unstructured organization Usefulness of cases based on

presence or absence of features Many cases Are Called – candidate

cases Few Are Chosen – structural

comparison between problem and possible solutions

Representational Approach

Pre-structured case base (indexing structure)

Neighboring cases are assumed to be similar

Probes constraints in memory to determine possible solutions

CBR for Innovative and Creative Design

Flexible case retrieval Retrieved cases show similar aspects to the

problem Different similarity measures have to be

dynamically composed during retrieval Fish and Shrink Algorithm

Structural similarity assessment Structural cases are processed and represented

as variables taking the role of problem or solution variables

Solution Transformation and Case Adaptation

New situations often different from old solutions

Solutions must be adapted to fit the constraints of the problem using parts from other past solutions

Solution Transformation and Case Adaptation

Three kinds of adaptation (Cunningham and Slattery 1993) Parametric adaptation – modifying

parameters Structural adaptation – adaptation

operators (grammar rules) Generative Adaptation – reuse and

adaptation for derivations of past problem-solving episodes

Fish and Shrink

Algorithm for flexible case retrieval Allows for rapid searching through

case base (even if significant aspects are combined at query time)

Can be stopped at any time and still produce usable results (though not complete)

Fish and Shrink

Similarity measure of emphasized attributes between all cases and a set of test cases are retrieved and stored

original case → αname → Ωname

Ωnamedistancesδname

T1

C1

Fish and Shrink (2)

Find similarity distance from test cases to problem Use predetermined similarity of cases to test cases to derive

the possible similarity of cases to problem Reduce similarity range to a single estimate by overlaying

similarity ranges to test case

Represents similarity distances between cases and emphasized attributes

Reduce range of possible similarity of any case to problem by utilizing the

predetermined similarity to test cases

Structural Similarity

Used to solve design problems involving a representative structure

Determines candidate solutions via maximal common subgraph (mcs)

Structural Similarity

Several functions are required Compile – translates attribute

representations of objects and relations into graphs

Recompile – converts graph back to attributes that may be depicted graphically

Retrieve – gets candidate cases Match – finds mcs between graphs

Structural Similarity

Best mcs transferred to problem Vertices and edges of other candidate

cases may be used to augment solution

Structural Similarity

Structural Similarity

Arrows represent spatial relations (touches, overlaps, etc)

Case Study – EADOCS

EADOCS Interactive, multi-level, and hybrid expert

system for aircraft sandwich panel structures

Structure of design defines the set of components, their configuration and parameter values

EADOCS (2)

Innovative design Plans for designing components are not

available Only partial models for evaluating

behavior are available

EADOCS (3)

Object Oriented class structure Design cases are instances of design

problems containing objects that define its behavior

For EADOCS, cases contain knowledge of the structural behavior of the design, such as an ability for a material to maintain its shape at a particular air pressure

EADOCS (4)

Retrieving a solution1. Best solutions are selected and

configured into prototype solutions2. A best prototype defining an optimal

design space is selected and a conceptual solution is retrieved

3. If no conceptual solution fitting the requirements can be retrieved, next best prototype is selected and 2 is repeated

EADOCS (5)

Case Combination Sub-targets are identified within the conceptual

solution that do not match the design requirements

New target for retrieval is defined Cases are retrieved to satisfy the new target Adaptations are retrieved based on differences

in functionality between cases with a similar structure to the conceptual solution and the case satisfying the new target

EADOCS (6)

Final Remarks

IDSS can significantly help with design tasks by: Decreasing design times by automating

aspects of the design process Increasing design quality by insuring

constraints of design are respected Improving the predictability of designs by

using learning algorithms to reduce design space

References

Arcos, J.L. and Enric Plaza. “The ABC of adaptation: Towards a Software Architecture for Adaptation-Centered CBR Systems.” 12 November 1999. 22 October 2006 <http://www.iiia.csic.es/Projects/cbr/ABC/abc-report.html>

Bergmann, Ralph. “Experience Management for Electronic Design Reuse.” Experience Management : Foundations, Development Methodology, and Internet-Based Applications. Springer Berlin/Heidelberg, 2002. 2 August 2003. 6 October 2006.

Börner, Katy. “CBR for Design.” Case-Based Reasoning Technology: From Foundations to Applications. Springer Berlin/Heidelberg, 1998. Springer Link. 20 May 2003. 6 October 2006.