\documentclass{article}

\title{Abstract: 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}
\maketitle

  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.

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