Learning control knowledge and case-based planning

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Learning control knowledgeand case-based
planning
Jim Blythe, with additional slides from
presentations by Manuela Veloso
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Motivation

• Planning is hard. PSpace-hard.
• BUT.. this is a worst-case result
• In many domains there may exist efficient
  strategies for planning
• May be able to derive them automatically from
  experience

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Controlling search

• Every planning algorithm does search
• Given a choice point, if makes incorrect choice,
  needs to backtrack and try other choices
• If we can make the right choice the first time

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Prodigy

• Explicit search control rules can apply to any
  decision point
• Many different learning approaches have been
  implemented
• Relatively old planning approach

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Learning methods in Prodigy
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Overview of Prodigy planning algorithm
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(No Transcript)
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Prodigy algorithm
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Prodigy algorithm, part II
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Decision points in Prodigy
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Example domain process planning
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Example control rules in Prodigy
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Review of explanation-based learning
MV

• Inputs
• Target concept definition
• Training example
• Domain theory
• Operationality criterion
• Output
• Generalization of the training example that is
• Sufficient to describe the target concept, and
• Satisfies the operationality criterion

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The safe-to-stack example
MV

• Input
• Target concept safe-to-stack(x,y)
• Training example
• on(obj1, obj2)
• isa(obj1, box) isa(obj2, endtable)
• color(obj1, red) color(obj2, blue)
• volume(obj1, 1) density(obj1, 0.1),

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The safe-to-stack example, cont.
MV

• Input
• Domain theory
• Not(fragile(y)) or lighter(x, y) gt
  safe-to-stack(x,y)
• Volume(x,v) and density(x,d) gt weight(x, vd)
• Weight(x1, w1) and weight(x2, w2) and less(w1,
  w2)
• gt lighter(x1, x2)
• Isa(x, endtable) gt weight(x, 5)
• Less(0.1, 5),
• Operationality criterion
• Learned description should use terms that
  describe objects directly, or are easy to
  evaluate, e.g less

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The safe-to-stack example
MV

• Explain why obj1 is safe-to-stack on obj2
• Construct a proof
• Do goal regression regress target concept
  through the proof structure
• Proof isolates relevant features

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Generating operational knowledge
MV

• Generalize proof
• Sometimes, simply replace constants by variables
• Prove that all identified relevant features are
  necessary in general
• Output
• volume(x,v1) and density(x,d1) and isa(y,
  endtable)
• and less(v1d1, 5)
• gt safe-to-stack(x,y)

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Using EBL to improve plan quality

• Given planning domain, evaluation function
• planners plan, a better plan
• Learn control knowledge to produce the better
  plan
• Explanation used explain why the alternative
  plan is better
• Target concept control rules that make choices
  based on the planner state and meta-state

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EBL in Prodigy

• Used by Minton (88) to improve efficiency of
  planning
• Version used in Quality (95) to improve quality
  of solution

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Architecture of Quality system
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Explaining better plans recursively
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Explaining better plans recursivelytarget
concept shared subgoal
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Example from process planning
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(No Transcript)
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Learned rules
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Discussion

• EBL is always correct, but Quality isnt only
  learns why plan B is better than plan A
• No guarantee of optimality
• Linear additive evaluation function how well
  does this model metrics we care about?
• Generality of control rules