Output details
11 - Computer Science and Informatics
University of Strathclyde
Output title
Evolutionary-based learning of generalised policies for AI planning domains
Type
E - Conference contribution
Name of conference/published proceedings
Proceedings of the 11th Annual conference on Genetic and evolutionary computation (GECCO '09)
Volume number
-
Issue number
-
First page of article
1195
ISSN of proceedings
-
Year of publication
2009
URL
-
Number of additional authors
3
Additional information
<22>The significance of this paper is that it isolates a large sub-class of planning domains that can be solved using a rule-based policy and further shows that evolutionary machine learning can create solvers that outperform the previous state-of-the-art solutions. Further evidence of significance is that the technique has been used by other researchers in solving hard sequential decision problems (e.g. by Sipper et al.). Finally this paper was published in the best conference in evolutionary computation (GECCO) and was cited by Pappa et al. as an exemplar of applying evolutionary machine learning to the problem of domain independent planning.
Interdisciplinary
-
Cross-referral requested
-
Research group
C - Software Systems
Citation count
3
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
