Output details
11 - Computer Science and Informatics
University of Huddersfield
Article title
Generation of macro-operators via investigation of action dependencies in plans
Type
D - Journal article
Title of journal
The Knowledge Engineering Review
Article number
-
Volume number
25
Issue number
03
First page of article
281
ISSN of journal
0269-8889
Year of publication
2010
Number of additional authors
0
Additional information
<22> Macro-operator learning is a technique that can speed up plan generation, but may also lead to search space explosion. This ground-breaking work shows how to avoid such combinatorial problems by reformulating planning tasks by adding macro-operators, and then removing replaced original operators. This paper was based on key results from the author's PhD thesis, was part of a project called “Merging machine learning and constraint satisfaction”(no.201/08/0509), funded by Czech Science Foundation, and has led to a rich stream of work on reformulation in AI planning.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
5
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
