A Heuristic Search Approach to Planning with Temporally Extended Preferences
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1808-1814.
Abstract
In this paper we propose a suite of techniques for planning with temporally extended preferences (TEPs). To this end, we propose a method for compiling TEP planning problems into simpler domains containing only final-state (simple) preferences and metric functions. With this simplified problem in hand, we propose a variety of heuristic functions for planning with final-state preferences, together with an incremental best-first planning algorithm. A key feature of the planning algorithm is its ability to prune the search space. We identify conditions under which our planning algorithm will generate optimal plans. We implemented our algorithm as an extension to the lplan ~planning system and report on extensive testing performed to evaluate the effectiveness of our heuristics and algorithm. Our planner, hplanp{}, competed in the 5th International Planning Competition, achieving distinguished performance in the emph{qualitative preferences} track.
@inproceedings{ijcai2007p292,
title = {A Heuristic Search Approach to Planning with Temporally Extended Preferences},
author = {Baier, Jorge A. and Bacchus, Fahiem and McIlraith, Sheila A.},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1808--1814},
year = {2007},
url = {https://ijcai.org/proceedings/2007/292},
}
TY - CPAPER TI - A Heuristic Search Approach to Planning with Temporally Extended Preferences AU - Baier, Jorge A. AU - Bacchus, Fahiem AU - McIlraith, Sheila A. T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1808 EP - 1814 UR - https://ijcai.org/proceedings/2007/292 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: