A Hybridized Planner for Stochastic Domains
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1972-1977.
Abstract
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabilistic planner based on the notion of hybridizing two algorithms. In particular, we hybridize GPT, an exact MDP solver, with MBP, a planner that plans using a qualitative (non-deterministic) model of uncertainty. Whereas exact MDP solvers produce optimal solutions, qualitative planners sacrifice optimality to achieve speed and high scalability. Our hybridized planner, HybPlan, is able to obtain the best of both techniques --- speed, quality and scalability. Moreover, HybPlan has excellent anytime properties and makes effective use of available time and memory.
@inproceedings{ijcai2007p318,
title = {A Hybridized Planner for Stochastic Domains},
author = {Mausam and Bertoli, Piergiorgio and Weld, Daniel S.},
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 = {1972--1977},
year = {2007},
url = {https://ijcai.org/proceedings/2007/318},
}
TY - CPAPER TI - A Hybridized Planner for Stochastic Domains AU - Mausam AU - Bertoli, Piergiorgio AU - Weld, Daniel S. T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1972 EP - 1977 UR - https://ijcai.org/proceedings/2007/318 ER -Copied
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