Concept Formation in a Design Optimization Tool

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• Concept Formation in a Design Optimization Tool
• Wei Peng and John S. Gero
• 7, July, 2006

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Outlines

• Design Optimization
• Concept formation
• Concept formation from a situated lens
• A situated agent-based design optimization tool
• The agents experience and concept formation
  engine
• Prototype system
• Testing results and future direction

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Design Optimization

• Three major tasks
• Interactive process
• Design knowledge requirement
• Application scenario how the agent learn to
  recognize design optimization problem

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Design Optimization Knowledge

• Recognition of appropriate optimization model is
• fundamental to design decision problems
• Can be expressed into semantic relationships
• between design elements
• For example
• Focus on learning and adapting the knowledge of
• recognizing an optimization problem

if all the variables are of continuous type and all the constraints are linear and the objective function in linear then conclude that the model is linear programming and execute linear programming algorithm
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Concept Formation (CF)

• Concept learning given a set of examples of
  some concept/class/category, determine if a given
  example is an instance of concept
• Concept formation incremental unsupervised
  acquisition of categories and their intentional
  descriptions
• Concept in designing a consequence of the
  situatedness of designing

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Concept Coupled Interactions in Designing
Virtual Knowledge Flows between two Worlds
Interactions in Designing
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Concept Formation through a Situated Lens

• Situatedness notion of conceptual situations
  that are based on the observers experiences and
  inseparable from interactions (Dewey, 1902)
• The concept formation process the way agent
• orders its experience in time (Clancey,1999)
  as conceptual
• coordination
• Concept formation framework in a situated agent
  
• (Gero and Fujii, 2000)

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Situated Concept Formation
Situated concept formation
Concept as higher order categorization of a
sequence
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A Constructive Memory Model
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A Situated Agent I

• A situated agent contains sensors, effectors,
  experience and a concept formation engine
• A concept formation engine consists of a
  perceptor, a cue_Maker, a conceptor, a
  hypothesizer, a validator and related processes
• Sense data takes the form of a sequence of
  actions and their initial descriptions
• S (t) click on objective function text
  field, key stroke of x, (, 1, ), ,
  x, (, 2, )
• Percepts are intermediate data structures of
  environment states with multimodal information.
  It can be described as (Objective Function
  Object, Objective_Function, x(1)x(2))

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A Situated Agent II

• Proto-concepts are initial or intermediate
  concept
• structures
• Tree or rule structures
• Hypotheses depict the agents explanations about
  failures in correctly predicting a situation
• Backward chaining rules
• Validation allows concepts and hypotheses to be
  evaluated in interactions
• Concepts are grounded proto-concepts or
  hypotheses
• Invariants about the agents experience

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Concept Formation I
Recast Concept Formation in A Constructive Memory
Model
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Concept Formation II
Recast Concept Formation in A Constructive Memory
Model
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Learning Scenario I
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System Architecture
Situated Agent-based Design Optimization Tool
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Learning Scenario II
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The Agents Experience
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The Experiential Response
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Grounding Experience I
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Grounding Experience II
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Prototype System
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Test I

• Using similar design tasks linear programming

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Test II

• Using novel design optimization scenarios
• L, Q, Q, L, NL, Q, NL, L, L, NL, Q, Q, L, L, L
• Initial experience a quadratic experience
• Behaviour charts and characteristics
• Performance (prediction rate) for a static,
  reactive and situated system

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Behaviour Charts
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Behaviour Characteristics
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Prediction Rates


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Summary and Future Work

• Concept formation in a situated agent
• New concept (new knowledge structure)
• Interaction plays a role in shaping structures
  and behaviours
• Co-evolution relation between structures and
  behaviours
• Future direction 1 maintaining user models in
  design interactions
• Future direction 2 learning from enriched
  contexts in design optimisation

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• The End
• Thanks!