Domain Model Acquisition in the Presence of Static Relations in the LOP System
Abstract
We present a new domain model acquisition algorithm, LOP, that induces static predicates by using a combination of the generalised output from LOCM2 and a set of optimal plans as input to the learning system. We observe that static predicates can be seen as restrictions on the valid groundings of actions. Without the static predicates restricting possible groundings, the domains induced by LOCM2 produce plans that are typically shorter than the true optimal solutions. LOP works by finding a set of minimal static predicates for each operator that preserves the length of the optimal plan. PDF
Cite
Text
Gregory and Cresswell. "Domain Model Acquisition in the Presence of Static Relations in the LOP System." International Joint Conference on Artificial Intelligence, 2016.Markdown
[Gregory and Cresswell. "Domain Model Acquisition in the Presence of Static Relations in the LOP System." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/gregory2016ijcai-domain/)BibTeX
@inproceedings{gregory2016ijcai-domain,
title = {{Domain Model Acquisition in the Presence of Static Relations in the LOP System}},
author = {Gregory, Peter and Cresswell, Stephen},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {4160-4164},
url = {https://mlanthology.org/ijcai/2016/gregory2016ijcai-domain/}
}