Not Logged In

Searching With Abstractions: A Unifying Framework and New High-Performance Algorithm

This paper presents a common algorithmic framework encompassing the twomain methods for using an abstract solution to guide search. It identifies certain keyissues in the design of techniques for using abstraction to guide search. Newapproaches to these issues give rise to newsearch techniques. Two of these are described in detail and compared experimentally with a standard search technique, classical refinement. The 'alternating opportunism' technique produces shorter solutions than classical refinement with the same amount of search, and is a more robust technique in the sense that its solution lengths are very similar across a range of different abstractions of anygivenspace.

Citation

R. Holte, C. Drummond, M. Perez, R. Zimmer, A. MacDonald. "Searching With Abstractions: A Unifying Framework and New High-Performance Algorithm". Canadian Conference on Artificial Intelligence (CAI), Banff, Canada, pp 263-270, January 1994.

Keywords: unifying, framework, machine learning
Category: In Conference

BibTeX

@incollection{Holte+al:CAI94,
  author = {Robert Holte and Chris Drummond and M.B. Perez and Robert Zimmer
    and Alan J. MacDonald},
  title = {Searching With Abstractions: A Unifying Framework and New
    High-Performance Algorithm},
  Pages = {263-270},
  booktitle = {Canadian Conference on Artificial Intelligence (CAI)},
  year = 1994,
}

Last Updated: June 18, 2007
Submitted by Staurt H. Johnson

University of Alberta Logo AICML Logo