Scope and Abstraction: Two Criteria for Localized Planning
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Planning. Pages 1612-1618.
Abstract
Localization is a general-purpose representational technique for partitioning a problem into subproblems. A localized problem-solver searches several smaller search spaces, one for each subproblem. Unlike most methods of partitioning, however, localization allows for subproblems that overlap - i.e. multiple search spaces may be involved in constructing shared pieces of the overall plan. In this paper we focus on two criteria for forming localizations: scope and abstraction. We describe a method for automatically generating such localizations and provide empirical results that contrast their use in an office-building construction domain .
@inproceedings{ijcai1995p208,
title = {Scope and Abstraction: Two Criteria for Localized Planning},
author = {Lansky, Amy L. and Getoor, Lise},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1612--1618},
year = {1995},
note = {Planning},
url = {https://ijcai.org/proceedings/1995/208},
}
TY - CPAPER TI - Scope and Abstraction: Two Criteria for Localized Planning AU - Lansky, Amy L. AU - Getoor, Lise T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1612 EP - 1618 UR - https://ijcai.org/proceedings/1995/208 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
