Case Adaptation Using an Incomplete Causal Model

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Case Adaptation Using an Incomplete Causal Model
John D.Hastings, L. Karl Branting, and Jeffrey A.
Lockwood, ICCBR, 1995
Integrating Cases and Models for Prediction in
Biological Systems, L. Karl Branting , John D.
Hastings Jeffrey A. Lockwood, AI Application,
under review An Empirical Evaluation of
Model-Based Case Matching and Adaptation, L.
Karl Branting , John D. Hastings, AAAI-94, 1994.

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What Why in CARMA

• CARMA (CAse-based Range Management Adviser)
  integrate CBR with MBR to predict the behavior of
  biological systems characterized both by
  incomplete models and insufficient empirical data
  for accurate induction
• determining the most cost-effective response to a
  given pest infestation requires prediction crop
  or forage loss under each available option

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Cont.

• use of model-based adaptation as a technique for
  integrating CBR with MBR in domains in which
  neither technique is individually sufficient for
  accurate prediction.
• Under this approach, Case-based reasoning is used
  to find an approximate solution into a more
  precise solution

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Process description

• Using RBR to infer the relevant facts of the
  infestation case.
• Determine whether grasshopper consumption will
  lead to competition with livestock for available
  forage
• Estimate the proportion of available forage that
  will be consumed by grasshoppers using CBR MBR
• Total the forage loss estimates for each subcase
  to predict the overall proportion of available
  forage that will be consumed by grasshoppers

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Cont.

• Compare grasshopper consumption with the
  proportion of available forager needed by
  livestock
• If competition, determine what possible treatment
  options should be excluded using rules
• If there are possible treatment options, for each
  one provided an economic analysis by estimating
  both the first-year and long-term savings using
  rule-based, model-based, and probabilistic
  reasoning

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Prototypical cases

• are not expressed in terms of observable
  features, but rather in terms of abstract derived
  features
• are extended in time, representing the history of
  a particular grasshopper population over its
  lifespan

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CARMA Step

• Determining relevant case features
• case matching
• model-based adaptation
• (x)case factoring
• temporal projection
• feature adaptation
• critical-period adjustment
• forage loss estimation
• determining treatment options
• treatment recommendation

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Model-based reasoning

• Case factoring
• spilt the overall population into subcases of
  grasshopper with distinct overwintering types
• Temporal projection
• retrieval all prototypical cases whose
  overwintering types match that of the subcase.
• CARMA must project the best matching prototypical
  case forward or backwards in time to align its
  average developmental phase with that of the new
  subcase
• CARMA breaks the distribution into daily
  populations, projects the populations the
  required number of days

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Featural adaptation

• Modify to account for any featural differences
  between it and the subcase.
• FL(NC) FL(PC) ? Ai QFD(i)
• QFD (Q(NC, i) - Q(PC, i)) / Q(PC, i)
• A adaptation weights, QFD quantitative
  difference for feature I between the new case and
  prototypical case

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Critical Period Adaptation

• Grasshopper consumption is most damaging if it
  occurs during the critical forage growing period
• must be adapted if the proportion of the lifespan
  of the grasshoppers overlapping the the critical
  period in the new case differs form that in the
  prototypical case

Ex subcaseA 47 case8 6 (47-6)/6
6.83 6.83 adapt weight
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Learning match and adaptation Weight

• Match weights (by system)
• determining the mutual information gain between
  case feature and qualitative consumption
  categories in a given set of training cases
• Featural adaptation
• use hill-climbing algorithm
• to min RMSE for prototypical case library P and
  match weights M, PFL CARMAs predocted forage
  loss, ExpertPred experts prediction of
  consumption for each training cases Ci

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Algorithm

• Function AdaptWeights(t, p, M)
• I lt initial increment
• Dmin lt minimum improvement threshold
• Imin lt minimum increment threshold
• A lt initial list of global adaptation weights
• D lt RMSE(T, P, M, A)
• D lt ?
• loop until (I lt Imin) do
• loop until (D-D lt Dmin) do
• D lt D
• ? lt the change to an element of A by I for
  which RMSE(T, P, M, ?(A)) is least
• D lt RMSE(T, P, M, ?(A))
• if (D lt D) then A lt ?(A) else D lt D
• I lt I/2
• return A

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Test function

• Fuction LeaveOneOutSpecificTest(T)
• for each case Ci?T do
• P T - Ci prototypical cases
• M global match weights for set P according to
  info. Gain
• for each prototypical case Pj?P do
• T P - Pj training set
• Pj(A) Adaptweights(T, Pj, M)
• Di (PredictForageLoss(Ci, P, M) -
  ExpertPred(Ci))2
• return((Avg(D)(1/2))
• Fuction LeaveOneOutGloubleTest(T)
• for each case Ci?T do
• P T - Ci prototypical cases
• M global match weights for set P according to
  info. Gain
• G Adaptweights(T, Pj, M)
• Di (PredictForageLoss(Ci, P, M, G) -
  ExpertPred(Ci))2
• return((Avg(D)(1/2))

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Schedule