Domain Model Acquisition in the Presence of Static Relations in the LOP System
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4160-4163.
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.
@inproceedings{ijcai2016p622,
title = {Domain Model Acquisition in the Presence of Static Relations in the LOP System},
author = {Gregory, Peter and Cresswell, Stephen},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4160--4163},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/622},
}
TY - CPAPER TI - Domain Model Acquisition in the Presence of Static Relations in the LOP System AU - Gregory, Peter AU - Cresswell, Stephen T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4160 EP - 4163 UR - https://ijcai.org/proceedings/2016/622 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
