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Design for IDSS
Liam Page
CSE 435
23 October 2006
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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
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What is design?
How does design help with:
decreasing design times?
increasing design quality?
improving design predictability?
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Classifying Design Task
Three classifications:
Routine Design
Innovative Design
Creative Design
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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
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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
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Complex Design
Non-routine design New variables
Extends/moves state space of potential designs
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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
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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)
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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
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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
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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
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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
• …
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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
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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
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Knowledge Representation
Four knowledge containers in CBR Vocabulary Case base Similarity measure Solution transformation
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Vocabulary
Vocabulary – task and domain dependent Should capture all important features of
design Supports problem solving in relevant
domain
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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
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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
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Similarity Measure
Two different approaches to similarity assessment Computational (similarity) approach Representational approach
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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
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Representational Approach
Pre-structured case base (indexing structure)
Neighboring cases are assumed to be similar
Probes constraints in memory to determine possible solutions
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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
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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
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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
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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)
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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
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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
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Structural Similarity
Used to solve design problems involving a representative structure
Determines candidate solutions via maximal common subgraph (mcs)
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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
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Structural Similarity
Best mcs transferred to problem Vertices and edges of other candidate
cases may be used to augment solution
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Structural Similarity
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Structural Similarity
Arrows represent spatial relations (touches, overlaps, etc)
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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
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EADOCS (2)
Innovative design Plans for designing components are not
available Only partial models for evaluating
behavior are available
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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
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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
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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
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EADOCS (6)
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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
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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.