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\title{Acquiring Domain-Specific Planners by Example}

\author{Elly Winner\\
  Computer Science Department\\
  Carnegie Mellon University\\
  5000 Forbes Avenue\\
  Pittsburgh, PA 15213-3890\\
  {\tt elly+@cs.cmu.edu}}

\date{}

\begin{document}

\begin{titlepage}
\maketitle

\centerline{\Large Thesis Proposal}
\bigskip

\begin{abstract}
  Intelligent problem solving requires the ability to select actions
  autonomously from a specific state to reach objectives. Planning
  algorithms provide approaches to look ahead and select a complete
  sequence of actions. Given a domain description consisting of
  preconditions and effects of the actions the planner can take, an
  initial state, and a goal, a planning program returns a sequence of
  actions to transform the initial state into a state in which the
  goal is statisfied. Classical planning research has addressed this
  problem in a domain-independent manner---the same algorithm
  generates a complete plan for any domain specification.  This
  feature comes at a cost which domain-independent planners incur
  either in high search efforts or in tedious hand-coded domain
  knowledge.
  
  Previous approaches to efficient general-purpose planning have
  focused on reducing the search involved in an existing
  general-purpose planning algorithm. An interesting alternative is to
  use example plans in a particular domain to demonstrate how to solve
  problems in that domain and to use that information to solve new
  problems independently of a domain-independent planner. Others have
  used example plans for case-based planning, but the retrieval and
  adaptation mechanisms were still domain-independent and efficiency
  issues were still a concern. 
  %In my thesis, I will introduce methods
  %for compiling example plans into a domain-specific planner.  The
  %challenges of this work are in revealing the planning choices behind
  %the example plans and in extracting a planning algorithm from that
  %information.
    
  In my thesis, I propose to introduce algorithms to extract complex,
  repeating processes, in the form of domain-specific planning
  programs, from example plans. I will investigate the application
  of these learned programs to modelling agent preferences and
  choices. I will also investigate how the programs can be used,
  extended, and repaired dynamically as an agent encounters new
  problems and acquires new experience. Finally, I will compare the
  template-based planning paradigm to existing general-purpose and
  domain-specific planning programs with a full evaluation on new and
  existing planning domains. I expect the core contribution of this
  thesis to be a new planning paradigm in which domain-specific
  planners are learned by example.

  
%  Planning is a powerful tool for action selection, since it offers a
%  guarantee that a proposed plan achieves an agent's goals.  If
%  efficient, agents could re-plan to deal with unexpected situations.
%  There is often a substantial amount of information available for
%  planners to exploit to increase efficiency: complete domain and
%  state descriptions are usually assumed in general-purpose planning,
%  and example plans are often easily accessible, either from human
%  teachers, model agents, past experience of the planner itself, or
%  plan databases.
  
%  Previous approaches to efficient general-purpose planning fall into
%  two categories: automatic and hand-coded. Automatic approaches have
%  relied on extracting control information from domain and example
%  plan analysis to reduce search and thereby speed up the planning
%  process. However, these approaches do not eliminate search, and
%  often suffer from the \textit{utility problem}: learning more
%  knowledge often slows down the planning process because of the extra
%  time involved in managing the knowledge. Hand-coded control
%  knowledge (or hand-written domain-specific planners) has proved more
%  useful, however it frequently requires weeks of work to find useful
%  rules.
  
%  However, in previous work, I have shown that example plans can
%  reveal more than control information: they can also reveal the
%  process behind their generation. I propose to introduce algorithms
%  to extract this process, in the form of a domain-specific planning
%  program, from example plans. I have already introduced {\sc Sprawl},
%  an algorithm for finding the rationale underlying observed example
%  plans. I have also introduced {\sc Distill}, an algorithm for
%  extracting simple non-repeating programs, or templates, from example
%  plans supplemented with their rationales. I plan to enrich the
%  template-description language used by {\sc Distill} to allow loops
%  and invariants and to develop methods of identifying and
%  extracting complex repeated structures in example plan bases. I will
%  investigate the application of these learned programs to modelling
%  agent preferences and choices. I will also investigate how the
%  programs can be used, extended, and repaired dynamically as an agent
%  encounters new problems and acquires new experience. Finally, I will
%  compare the template-based planning paradigm to existing
%  general-purpose and domain-specific planning programs with a full
%  evaluation on new and existing planning domains.
\end{abstract}

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\end{titlepage}

\tableofcontents

\include{introduction}
\include{background}
\include{previous}
\include{proposed}

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\end{document}