\documentclass[]{article}
\usepackage{aaai}
\usepackage{times}
\usepackage{graphicx}
\usepackage{latexsym}
\usepackage{epic,eepic,epsfig}
\include{epsf}

\newcommand{\ang}[1]{#1^{\circ}}
\newcommand{\mywhile}{{\bf while} }
\newcommand{\myif}{{\bf if} }
\newcommand{\mythen}{{\bf then} }
\newcommand{\mydo}{{\bf do} }
\newcommand{\myreturn}{{\bf return} }
\newcommand{\myfor}{{\bf for} }
\newcommand{\myuntil}{{\bf until} }
\newcommand{\myelse}{{\bf else} }
\newcommand{\mynot}{{\bf not} }
\newcommand{\myand}{{\bf and} }
\newcommand{\myor}{{\bf or} }
\newcommand{\mytrue}{{\bf true} }
\newcommand{\myfalse}{{\bf false} }
\newcommand{\myprocedure}{{\bf procedure} }
\newcommand{\mygoal}{{\bf goal state} }
\newcommand{\mycur}{{\bf current state} }
\newcommand{\mysame}{{\bf same} }
\newcommand{\ingoal}{{\bf in\_goal\_state} }
\newcommand{\incur}{{\bf in\_current\_state} }
\newcommand{\ininit}{{\bf in\_initial\_state} }
\newcommand{\suchthat}{{\bf such that} }
\newcommand{\myassign}{$\leftarrow$ }
\newcommand{\distill}{{\sc Distill} }
\newcommand{\fixme}[1]{{\bf #1}}

\graphicspath{{figs/}}

\title{DISTILL: Automated Generation of Domain-Specific Planners}

\author{Elly Winner \and Manuela Veloso \\
	Computer Science Department,Carnegie Mellon University,\\
	5000 Forbes Avenue, Pittsburgh,PA 15213-3891, USA\\
         \{elly,mmv\}@cs.cmu.edu}		


\begin{document}
\maketitle

\begin{abstract}
  Domain-independent 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. We are instead pursuing research in
  which In this paper, we present introduce an algorithm to create
  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.

\end{abstract}

\input{introduction}
\input{background}
\input{previous-dsplanner}

\bibliography{../../bibtex/all}
\bibliographystyle{aaai}
\end{document}
