Not Logged In

Learning Partial-Order Macros from Solutions

Full Text: icaps05.pdf PDF

Despite recent progress in AI planning, many problems remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present an automated method that learns relevant information from previous experience in a domain and uses it to solve new problem instances. Our approach produces a small set of useful macro-operators as a result of a training process. For each training problem, a structure called a solution graph is built based on the problem solution. Macro-operators with partial ordering of moves are extracted from the solution graph. A filtering and ranking procedure selects the most useful macro-operators, which will be used in future searches. We introduce a heuristic technique that uses only the most promising instantiations of a selected macro for node expansion. Our results indicate an impressive reduction of the search effort in complex domains where structure information can be inferred.

Citation

A. Botea, M. Mueller, J. Schaeffer. "Learning Partial-Order Macros from Solutions". ICAPS, pp 231-240, January 2005.

Keywords: machine learning
Category: In Conference

BibTeX

@incollection{Botea+al:ICAPS05,
  author = {Adi Botea and Martin Mueller and Jonathan Schaeffer},
  title = {Learning Partial-Order Macros from Solutions},
  Pages = {231-240},
  booktitle = {},
  year = 2005,
}

Last Updated: January 04, 2007
Submitted by Christian Smith

University of Alberta Logo AICML Logo