On the Online Generation of Effective Macro-operators

Chrpa, Lukáš, Vallati, Mauro and McCluskey, T.L. (2015) On the Online Generation of Effective Macro-operators. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. AAAI Press, pp. 1544-1550. ISBN 9781577357384

PDF - Accepted Version Download (361kB)

Abstract

Macro-operator (“macro”, for short) generation is a
well-known technique that is used to speed-up the
planning process. Most published work on using
macros in automated planning relies on an offline
learning phase where training plans, that is, solutions
of simple problems, are used to generate the
macros. However, there might not always be a place
to accommodate training.
In this paper we propose OMA, an efficient method
for generating useful macros without an offline
learning phase, by utilising lessons learnt from existing
macro learning techniques. Empirical evaluation
with IPC benchmarks demonstrates performance
improvement in a range of state-of-the-art
planning engines, and provides insights into what
macros can be generated without training.

Item Type:	Book Chapter
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Schools:	School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
Related URLs:	Author Conference
Depositing User:	Lukas Chrpa
Date Deposited:	14 May 2015 15:40
Last Modified:	10 Dec 2016 02:06
URI:	http://eprints.hud.ac.uk/id/eprint/24492

Downloads

Repository Staff Only: item control page

View Item