Design for IDSS

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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 -gt 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 users 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

• Transformed Solution
• Dimensions 30 x 30
• Doors 1
• Outlets 6
• Deluxe Standing Shower yes

• Case DeluxeBathroom2
• Dimensions ( 30 50)x (30x50)
• Doors 2 3
• Outlets 6 10
• Deluxe Standing Shower yes

• Case DeluxeBathroom1
• Dimensions (20-40)x (20-40)
• Doors 1 2
• Outlets 4 6
• Hot tub 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
original case ? aname ? Oname
T1
dname
Oname
distances
C1

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

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Fish and Shrink (2)
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

• 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

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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 solution
• Best solutions are selected and configured into
  prototype solutions
• A best prototype defining an optimal design space
  is selected and a conceptual solution is
  retrieved
• 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 lthttp//www.iiia.csic.es/Pr
  ojects/cbr/ABC/abc-report.htmlgt
• 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.