Prioritized Goal Decomposition of Markov Decision Processes: Toward a Synthesis of Classical and Decision Theoretic Planning
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Planning and Scheduling. Pages 1156-1161.
Abstract
We describe an approach to goal decomposition for a certain class of Markov decision processes (MOPs) . An abstraction mechanism is used to generate abstract MOPs associated with different objectives, and several methods for merging the policies for these different objectives are considered. In one technique, causal (least-commitment) structures are generated for abstract policies and plan merg ing techniques, exploiting the relaxation of policy commitments reflected in this structure, are used to piece the results into a single policy. Abstract value functions provide guidance if plan repair is needed. This work makes some first steps toward the synthesis of classical and decision theoretic planning methods .
@inproceedings{ijcai1997p167,
title = {Prioritized Goal Decomposition of Markov Decision Processes: Toward a Synthesis of Classical and Decision Theoretic Planning},
author = {Boutilier, Craig and Brafman, Ronen I. and Geib, Christopher W.},
booktitle = {Proceedings of the Fifteenth International Joint Conference on
Artificial Intelligence, {IJCAI-97}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Martha E. Pollack},
pages = {1156--1161},
year = {1997},
note = {Planning and Scheduling},
url = {https://ijcai.org/proceedings/1997/167},
}
TY - CPAPER TI - Prioritized Goal Decomposition of Markov Decision Processes: Toward a Synthesis of Classical and Decision Theoretic Planning AU - Boutilier, Craig AU - Brafman, Ronen I. AU - Geib, Christopher W. T2 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97 PB - International Joint Conferences on Artificial Intelligence PY - 1997 SP - 1156 EP - 1161 UR - https://ijcai.org/proceedings/1997/167 ER -Copied
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