Poordefinition, Uncertainty and Human Factors A Case for Interactive Evolutionary Problem Reformulat

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Poor-definition, Uncertainty and Human Factors -
A Case for Interactive Evolutionary Problem
Reformulation? I. C. Parmee Advanced
Computational Technologies, Exeter, UK.
iparmee_at_ad-comtech.co.uk
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• Setting the Scene
• Ill-definition, uncertainty and multiple
  objectives - primary characteristics of
  real-world decision-making processes.
• During initial stages little knowledge of
  problem at hand may be available.
• Primary task - improve problem definition in
  terms of variables, constraint and quantitative
  and qualitative objectives.
• Problem space can develop with information
  gained
• Dynamical process - optimisation plays a
  secondary role following establishment of
  well-defined problem domain.
• Presentation speculates upon role of
  evolutionary computing, complementary
  computational intelligence techniques and
  interactive systems that support problem
  definition

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• Conceptual Design
• Main area of interest - evolutionary engineering
  design particularly higher levels of design
  process
• Conceptualisation represents highly complex
  human-centred activity supported by basic
  machine-based models of the problem domain.
• Search across ill-defined space of possible
  solutions - fuzzy objective functions, vague
  concepts of structure of final solution.
• Solutions / partial solutions explored and
  assessed with regard to constraints and
  objectives considered relevant at that time.
• Heuristics, approximation and experimentation -
  major role
• Flexibility evident in establishment of domain
  bounds, objectives and constraints.

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• Design environment evolves with the solutions
  as designer gains understanding of functional
  requirements and possible structures.
• Simple human / computer-based models - largely
  qualitative in nature -utilised to establish
  initial direction.
• Decision-making environment characterised by
  uncertainty in terms of lack of available data
  and a poorly defined initial specification.
• Discovery and accumulation of knowledge
  appertaining to problem definition and objective
  preferences prevalent in highly dynamical human /
  machine-based process.

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Quote from Goel ...problem formulation and
reformulation are integral parts of creative
design. Designers understanding of a problem
typically evolves during creative design
processing. This evolution of problem
understanding may lead to (possibly radical)
changes in the problem and solution
representations. . in creative design,
knowledge needed to address a problem typically
is not available in a form directly applicable to
the problem. Instead, at least some of the
needed knowledge has to be acquired from other
knowledge sources, by analogical transfer from a
different problem for example. creativity in
design may occur in degrees, where the degree of
creativity may depend upon the extent of problem
and solution reformulation and the transfer of
knowledge from different knowledge sources to the
design problem. Goel A. K., Design, Analogy and
Creativity. IEEE Expert, Intelligent Systems and
their Applications, 12(3). (1997) 62 70)
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• Changing Objectives During Decision-making
• Discovery and knowledge accumulation aspects
  common across decision-making.
• Exploration will likely result in re-formulation
  of the problem domain through iterative search
  and analysis of identified solutions.
• For illustrative purposes consider a job-related
  relocation to a new city and the daunting problem
  of finding a family home
• Initial investigation - identifying appropriate
  districts based upon criteria relating to
• quality of local schools
• safety / security issues
• proximity to places of work, transport, highway
  networks, shopping and leisure facilities etc.
• average price and type of housing and overall
  environment.

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• Other criteria relate directly to the ideal
  property e.g.
• maximum cost
• number of bedrooms
• garden, garage, parking etc.
• Several criteria would be considered hard
  constraints (i.e. maximum cost) in the first
  instance.
• Decision-making team is the family - each
  probably rate the relative importance of the
  above criteria in a slightly different manner -
  opinions of each member will carry a varying
  degree of influence.
• Likely that initially there is a pretty clear
  vision of what the ideal property will look like
  and the preferred location.

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• Information Gathering
• Initial information gathering provides
  quantitative and qualitative data relating to
  location from wide variety of sources some
  reliable and some based upon hearsay.
• Gradually overall picture is established -
  results in elimination of some options and
  inclusion of new possibilities.
• New possible locations discovered during
  explorative trips to those already identified.
• Possible change in preferences relating to
  property type, style etc as new options arise

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• Concept of Compromise and Problem Re-definition
• As property details are gathered - likely
  apparent that ideal solution is hard to find -
  concept of compromise becomes a reality.
• Hard constraints may soften
• objective preferences will constantly be
  discussed and re-defined in the light of
  accumulated knowledge regarding districts and
  property availability within them.
• Particular characteristics of areas initially
  thought unsuitable may suddenly appear
  attractive.
• Search concentration may shift with discovery
  that such areas have suitable properties within
  the pre-set price range.

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• Initial hard constraint on max. price may soften
  as close to ideal properties in favoured
  locations become available. Other compromises
  are explored to accommodate increased costs.
• Process becomes uncertain mix of subjective /
  objective decisions as goal-posts move,
  objectives rapidly change in nature and external
  pressures (time constraints?) begin to take
  precedence.
• Quite probable that chosen home differs
  significantly from the one first envisaged e.g.
• Location is ideal - guest bedroom is sacrificed,
  garden is minute but the second car has to go.
• The period town-house has become a modern
  detached but the budget is intact.
• Route to work may be longer but property close
  to ideal at a good price in an up-and-coming
  neighbourhood has been found.

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• Problem Commonalities
• Although a seemingly simple problem overall
  search process is highly complex - uncertainty,
  compromise and problem re-definition inherent
  features.
• Although differing from commercial and
  industrial decision-making scenarios analogies
  are apparent.
• Much can be learnt much from everyday
  decision-making scenarios and this knowledge can
  be utilised when designing interactive
  evolutionary search environments that can support
  complex decision-making processes.

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• Knowledge Generation and Extraction
• Machine-based search and exploration environment
  that provides problem information to the designer
  / decision-making team is required
• . Processing of such information and discussion
  results in recognition of similarities with other
  problem areas and discovery of alternative
  approaches.
• Major characteristic of population-based search
  is the generation of much possibly relevant
  information most of which is discarded.
• Development of interactive systems supports
  capture of such information and utilisation in
  re-formulation of problem through application
  and integration of experiential knowledge.
• Can this knowledge be embedded in further
  evolutionary search relating to the re-defined
  problem?

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• Re-definition of objectives / objective
  preferences important aspect of evolution of the
  problem space.
• Primary role of evolutionary machine-based
  search and exploration processes can be
  generation of information.
• Moves utilisation of EC away from application
  over set number of generations to a continuous
  exploratory process where changes to objectives,
  variable ranges and constraint based upon
  information generated.
• Results in a moving, evolving problem space
  where primary task is design of an optimal
  problem space
• Theme has been central to much previous work
  leading to establishment of an interactive
  evolutionary design system (IEDS) that supports
  relatively continuous, iterative user /
  evolutionary search process

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• Earlier Work
• Theme has been central to previous work -
  development of EC strategies relating to the
  higher levels of the design process has related
  to
• identification of high performance regions of
  complex conceptual design space (vmCOGAs)
• identification of optimal alternative system
  configurations through utilisation of dual-agent
  strategies for search across mixed discrete /
  continuous decision hierarchies.
• Other work relates to the concurrent satisfaction
  of both quantitative and qualitative criteria
  through the integration of fuzzy rule bases with
  evolutionary search.

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• The Evolutionary Interactive Design System
• Requirement for system that supports on-line
  extraction of information that supports easily
  implemented change
• Investigation of various techniques that can be
  combined within an overall architecture.
• Satisfaction of multiple objectives (i.e.gt 10)
  major requirement
• Objectives must be very flexible re preferences
  / weightings to allow exploration of problem
  domain - supports better understanding of complex
  interactions between variable space and objective
  space.

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Co-evolutionary / Stand-alone Multi-objective
Processes
Information Gathering Processes COGAs Taguchi etc
Linguistic Preferences / Objective Weighting
Decision-maker / Designer
Components of the Interactive Evolutionary Design
System
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Interactive Evolutionary Design System
On-line Database
Rule-Based Preferences
Scenario (A) Evolution
Information gathering processes
Machine-Based Agents
Scenario (C) Evolution
Scenario (B) Evolution
External Agents (Design Team)
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Two modes of operation Mode 1 Much uncertainty
re problem domain coarse model of system under
design little knowledge of relative importance
of objectives / constraints prime variables or
variable ranges. Requirement exploration of
initial design space to gather information re
above. Method introduce either single evolution
relating to one objective or multiple evolutions
each relating to differing objective Extract
optimal information during evolutionary process
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Information Gathering via Cluster-oriented
Genetic Algorithms (COGAs)

• Developed to
• Rapidly decompose complex conceptual design
  space into regions of high performance
• Support extraction of relevant design
  information from such regions through good
  solution cover.
• Provide a greater understanding of
  multi-objective interaction
• Indicate best direction during early stages of
  design

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• How?
• Highly explorative GA / GAs
• Solutions extracted and passed through Adaptive
  Filter
• Better solutions pass into Final Clustering Set
  - defines HP regions

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• Design Environment
• Preliminary design of military air frames with
  BAE
• Uncertain requirements and fuzzy objectives -
  long gestation periods between initial design
  brief and realisation of the product.
• changes in operational requirements and
  technolo-gical advances - responsive, highly
  flexible strategy required - design change and
  compromise inherent features
• Design exploration leading to innovative and
  creative activity must be supported.

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• CAPS (Computer Aided Design Studies), a BAE
  suite of preliminary design models utilised to
  support airframe design.
• MiniCAPS - much abridged version of CAPS used
  for experimentation purposes - retains major
  characteristics of overall requirements.
• 9 input variables, eleven outputs relating to a
  range of objectives.

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Application of COGA to Preliminary Airframe
Design
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Figures 1 to 4 show the effect of increasing the
filter threshold setting. Low settings of figure
1 result in a large cluster of medium fitness
solutions increasing the filter setting results
in the identification of the two disjoint
clusters of figure 4.
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COGA applied to differing internal geometries of
turbine cooling hole problem
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High-performance regions relating to various
objectives
a
b
c
Lines define boundaries of the high performance
regions for each objective - shaded area defines
common region containing HP solutions that
satisfy more than one objective. (a) Common
region containing high performance solutions for
Ferry Range and Turn Rate identified but Specific
Excess Power(SEP)cannot be satisfied.
(b)Relaxing filter threshold for SEP allows lower
fitness SEP solutions through, boundary moves
towards feasible region (c) Further relaxation
results in the identification of a feasible
region for all objectives.
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• Mode 2
• Having established better understanding of
  design domain in terms of
• Relative sensitivity of objectives to each
  variable and any variable redundancy
• Appropriate variable ranges
• Degree of conflict between objectives,
  objective redundancy, indication of objective
  satisfaction difficulties
• Solution distribution, design space
  characteristics
• Designer can modify design space, set objective
  preferences and establish more definitive
  multi-objective GA-based search. Process still
  continuous with on-line variation of design
  space, design scenarios and objective preferences.

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Rule-based Objective Preferences

• Simple linguistic rules facilitate direct
  preference manipulation by the designer e.g
• relation intended meaning
• ? is equally important
• lt is less important
• ltlt is much less important
• gt is more important
• gtgt is much more important
• Ranked preferences relating to
  multi-objectives can be introduced and altered
  during an evolutionary run.
• Designer only required to answer a minimal set
  of straightforward questions
• Preferences transformed into numerical objective
  weightings

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Co-evolutionary Multi-objective Satisfaction

• How?
• Concurrent GA processes each optimise one
  objective
• Fitness measure for individuals within each GA
  is adjusted by comparing distance between
  solutions of one objective with those of others
• Penalty relating to the degree of diversity of
  variables of each objective process is imposed
  taking into consideration a generational
  constraint map
• Initial convergence upon individual objectives
  leads to overall convergence of all processes
  upon a single compromise design region.
• On-line sensitivity analysis utilising Taguchi
  ensures relative importance of a parameter is
  taken into account .

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Range Constraint Maps
(a)
(b)
(c)
(d)
Initially map allows each GA to produce
solutions based on own objective As run
progresses the map, through inflicted penalties,
reduces variable diversity to draw all
concurrent GA searches from separate objectives
towards a single compromise region. Maps include
a linear decrease in range constraint and a range
constraint reduction based on a sine curve.
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• Uncertain Data and Approximate Results
• Aim is to support better understanding of
  objective interaction / conflict through
  graphical representation rather than accurately
  defining regions of the Pareto frontier.
• Technique supports generation of information
  where variables and objectives and can vary as
  problem knowledge expands.
• Approach takes into consideration uncertainties
  and ill-definition inherent in preliminary design
  models and degree of initial understanding of
  problem domain.
• Approach considered more viable than utilisation
  of more sophisticated techniques that identify
  optimal non-dominated solutions that lie upon the
  true Pareto frontier at this stage.
• Notion of rubbish in, rubbish out must be
  taken into consideration.

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On-line Database
Rule-Based Preferences
Scenario (A) Evolution
Information gathering processes
Machine-Based Agents
Scenario (C) Evolution
Scenario (B) Evolution
External Agents (Design Team)
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A
B
Preferences and Co-evolutionary MOGA combined (a)
Ferry Range is much more important b) All
objectives are of equal importance (c) Ferry
Range is much less important
C
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Agents for Scenario / Dynamical Constraint
Satisfaction

• Designer likely to have several ideal scenarios
  such as I would like objective A to be
  greater than 0.6 and objective C to be less than
  83.56 objectives B, D, E should be maximised
  variable 2 should have a value of between 128.0
  and 164.5 a value graeter than o.32 is prefered
  for variable 7
• Incremental Agent operates as follows
• 1 Use designers original preferences for both
  objectives and scenarios and run optimisation
  process
• 2 If some scenarios are not fulfilled, agent
  suggests increase in their importance of these
  scenarios
• 3 If some scenarios still not fulfilled even
  when classed as most important agent suggests
  change to variable ranges in scenario.
• 4 If some scenarios still not fulfilled agent
  reports to designer and asks for assistance.

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Agent Co-operation

• Consider a system with several agents each
  trying to optimise a single objective
• Each agent is aware of the quality of its own
  solution
• If agent 1 solution is inferior and
  contradicting to others, agent 1 should
  compromise and accept worse solution to benefit
  group as a whole
• If agents cant decide, user is consulted.
• If user resolves conflict agents remember
  decision for next time
• Inter agent polling based upon objective and
  scenario preferences utilised to resolve
  conflicts

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Closed preference / scenario loop
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• Summary
• Real-world multi-objective decision-making
  processes where problem domain develops with
  information gained
• EC can support such processes through highly
  interactive systems
• generated information provides problem insights
  supporting problem reformulation.
• Initial framework briefly outlined - relatively
  seamless development of problem space where the
  decision-makers knowledge becomes embedded
  within an iterative human / evolutionary
  computational process ultimate goal
• Concept moves away from identification of
  non-dominated solutions and generation of an
  n-dimensional Pareto frontier.

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• Inherent uncertainties and human-centred aspects
  of complex decision-making environments renders
  such approaches less viable - utility
  well-founded in more well-defined problem areas.
• Moves away from identification of solutions
  through short-term application of evolutionary
  search techniques.
• Continuous, dynamic explorative process - search
  and exploration capabilities of iterative
  designer / evolutionary systems.
• Concept could best utilise processing
  capabilities of present and future computing
  technology during complex human / machine-based
  decision-making activities.
• Further research - much modified structure where
  agent technologies play a major and, to some
  extent, autonomous role to ensure appropriate
  communication and information processing
  capabilities.