Sequencing Operator Counts
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4140-4143.
Abstract
Operator-counting is a recently developed framework for analysing and integrating many state-of-the-art heuristics for planning using Linear Programming. In cost-optimal planning only the objective value of these heuristics is traditionally used to guide the search. However the primal solution, i.e. the operator counts, contains useful information. We exploit this information using a SAT-based approach which given an operator-count, either finds a valid plan; or generates a generalized landmark constraint violated by that count. We show that these generalized landmarks can be used to encode the perfect heuristic, h*, as a Mixed Integer Program. Our most interesting experimental result is that finding or refuting a sequence for an operator-count is most often empirically efficient, enabling a novel and promising approach to planning based on Logic-Based Benders Decomposition (LBBD).
@inproceedings{ijcai2016p618,
title = {Sequencing Operator Counts},
author = {Davies, Toby O. and Pearce, Adrian R. and Stuckey, Peter J. and Lipovetzky, Nir},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4140--4143},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/618},
}
TY - CPAPER TI - Sequencing Operator Counts AU - Davies, Toby O. AU - Pearce, Adrian R. AU - Stuckey, Peter J. AU - Lipovetzky, Nir T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4140 EP - 4143 UR - https://ijcai.org/proceedings/2016/618 ER -Copied
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