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@INPROCEEDINGS{KhalvatiAAAI2013,
author={Koosha Khalvati and Alan K. Mackworth},
booktitle={Proceedings of The Twenty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2013},
title={A Fast Pairwise Heuristic for Planning under Uncertainty},
year={2013},
pages={187-193},
abstract={POMDP (Partially Observable Markov Decision Process) is
a mathematical framework that models planning under uncertainty.
Solving a POMDP is an intractable problem and
even the state of the art POMDP solvers are too computationally
expensive for large domains. This is a major bottleneck.
In this paper, we propose a new heuristic, called the pairwise
heuristic, that can be used in a one-step greedy strategy to find
a near optimal solution for POMDP problems very quickly.
This approach is a good candidate for large problems where
real-time solution is a necessity but exact optimality of the
solution is not vital. The pairwise heuristic uses the optimal
solutions for pairs of states. For each pair of states in the
POMDP, we find the optimal sequence of actions to resolve
the uncertainty and to maximize the reward, given that the
agent is uncertain about which state of the pair it is in. Then
we use these sequences as a heuristic and find the optimal action
in each step of the greedy strategy using this heuristic.
We have tested our method on the available large classical
test benchmarks in various domains. The resulting total reward
is close to, if not greater than, the total reward obtained
by other state of the art POMDP solvers, while the time required
to find the solution is always much less.},
bib2html_pubtype ={Refereed Conference Proceeding},
bib2html_rescat ={},
}