Fast Planning with Iterative Macros
- Adi Botea
- Martin Mueller
- Jonathan Schaeffer, Department of Computing Science, University of Alberta
Research on macro-operators has a long history in planning and other search applications. There has been a revival of interest in this topic, leading to systems that successfully combine macrooperators with current state-of-the-art planning approaches based on heuristic search. However, research is still necessary to make macros become a standard, widely-used enhancement of search algorithms. This article introduces sequences of macro-actions, called iterative macros. Iterative macros exhibit both the potential advantages (e.g., travel fast towards goal) and the potential limitations (e.g., utility problem) of classical macros, only on a much larger scale. A family of techniques are introduced to balance this trade-off in favor of faster planning. Experiments on a collection of planning benchmarks show that, when compared to low-level search and even to search with classical macro-operators, iterative macros can achieve an impressive speed-up in search.
Citation
A. Botea, M. Mueller, J. Schaeffer. "Fast Planning with Iterative Macros". International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, March 2007.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Botea+al:IJCAI07,
author = {Adi Botea and Martin Mueller and Jonathan Schaeffer},
title = {Fast Planning with Iterative Macros},
booktitle = {International Joint Conference on Artificial Intelligence
(IJCAI)},
year = 2007,
}Last Updated: April 24, 2007Submitted by Nelson Loyola
