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\title{Proposal Outline: Acquiring Domain-Specific Planners from
  Example Plans}

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\begin{enumerate}
 \item Introduction
  \begin{enumerate}
   \item Motivation
    \begin{enumerate}
     \item Given plans that demonstrate how to solve a domain, learn program
     \item (Agent modelling)
     \item G-P planning: solvability horizon, speed
     \item Learned control too big, therefore too slow (utility prob)
     \item Handwritten control must be... hand written!
     \item Can often get ``free'' observed solutions, at least for
      simple probs
     \item Use to learn d-s planner--they're faster
     \item Discuss sound, complete, optimal tradeoffs, etc
    \end{enumerate}
   \item Approach
    \begin{enumerate}
     \item Use Observed Problems/Solutions as Source for Learning
     \item Analyze Examples
     \item Extract ``Program'' (/Template/Domain-Specific Planner)
      from Examples
     \item Use D-S Planner to Solve New Problems
    \end{enumerate}
   \item Expected Contributions
    \begin{enumerate}
     \item Domain-independent algorithm for extracting rationale from
      example plans
     \item Algorithm for learning domain-specific planners from
      example plans
    \end{enumerate}
  \end{enumerate}
 \item Background
  \begin{enumerate}
   \item Plan Analysis
    \begin{enumerate}
     \item Triangle Tables
     \item Validation Structures
     \item Derivational Analogy
     \item Partially Ordering Total Orders
     \item Partial-Order Planning
    \end{enumerate}
   \item Domain Knowledge for Planning
    \begin{enumerate}
     \item Control Rules
     \item Macro-Operators
     \item Skeletal Plans \& Abstraction Hierarchies
     \item Case-Based \& Analogical Planning
     \item Domain-Specific Planners
    \end{enumerate}
   \item Program Learning
    \begin{enumerate}
     \item Iterative and Recursive Macro-Operators
     \item Program Learning from Specifications
     \item Inductive Program Synthesis
    \end{enumerate}
  \end{enumerate}
 \item Previous Work
  \begin{enumerate}
   \item {\textsc Sprawl}
   \item {\textsc Distill}
  \end{enumerate}
 \item Proposed Work
  \begin{enumerate}
   \item Definitely Will Do
    \begin{enumerate}
     \item Discuss sound, complete, optimal tradeoffs, etc
     \item Algorithm to acquire rich templates from example plans
      \begin{enumerate}
       \item Extract Loops from Nice Examples
       \item Extracting Loops from Slightly-Less-Nice Examples (simplification)
       \item Merging Templates with Loops
      \end{enumerate}
     \item Use and maintenance of the programs
      \begin{enumerate}
       \item Current method
       \item Repairing Incorrect Templates
       \item Active Learning
      \end{enumerate}
    \end{enumerate}
   \item Will Also Dabble In
    \begin{enumerate}
     \item Agent Modelling (undersimplifying)
     \item Examples of domains that can be covered
     \item When template doesn't solve whole problem, can we use
      parts?
    \end{enumerate}
   \item Evaluation
    \begin{enumerate}
     \item Test on existing domains + more
      \begin{enumerate}
       \item Generate domains with choices to demonstrate modelling
       \item Look for other domains for which it'd be good?
      \end{enumerate}
     \item Test for:
      \begin{enumerate}
       \item Possible to cover whole domain?
       \item Number of examples to cover whole domain
       \item Restrictions on examples for learning
       \item Ability to learn program from examples produced by the program
       \item Efficacy of learning on examples produced by various planners
       \item Efficacy for agent modelling (Ability to capture \textit{preferences})
       \item Speed vs generative planning
       \item Max problem size handled vs generative planning
       \item Efficiency of plans vs generative planning
      \end{enumerate}
    \end{enumerate}
  \end{enumerate}
\end{enumerate}

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