Heuristic Guidance for Forward-Chaining Planning with Numeric Uncertainty. Marinescu, L. & Coles, A. In Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling (ICAPS 2016), pages 230–234, 2016. ISSN: 09226389
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Uncertainty hinders many interesting applications of planning – it may come in the form of sensor noise, unpredictable environments, or known limitations in problem models. In this paper we explore heuristic guidance for forward-chaining planning with continuous random variables, while ensuring a probability of plan success. We extend the Metric Relaxed Planning Graph heuristic to capture a model of uncertainty, providing better guidance in terms of heuristic estimates and dead-end detection. By tracking the accumulated error on nu- meric values, our heuristic is able to check if preconditions in the planning graph are achievable with a sufficient degree of confidence; it is also able to consider acting to reduce the accumulated error. Results indicate that our approach offers improvements in performance compared to prior work where a less-informed relaxation was used.
@inproceedings{marinescu_heuristic_2016,
	title = {Heuristic {Guidance} for {Forward}-{Chaining} {Planning} with {Numeric} {Uncertainty}},
	isbn = {978-1-61499-671-2},
	doi = {10.3233/978-1-61499-672-9-1694},
	abstract = {Uncertainty hinders many interesting applications of planning – it may come in the form of sensor noise, unpredictable environments, or known limitations in problem models. In this paper we explore heuristic guidance for forward-chaining planning with continuous random variables, while ensuring a probability of plan success. We extend the Metric Relaxed Planning Graph heuristic to capture a model of uncertainty, providing better guidance in terms of heuristic estimates and dead-end detection. By tracking the accumulated error on nu- meric values, our heuristic is able to check if preconditions in the planning graph are achievable with a sufficient degree of confidence; it is also able to consider acting to reduce the accumulated error. Results indicate that our approach offers improvements in performance compared to prior work where a less-informed relaxation was used.},
	booktitle = {Proceedings of the {Twenty}-{Sixth} {International} {Conference} on {Automated} {Planning} and {Scheduling} ({ICAPS} 2016)},
	author = {Marinescu, Liana and Coles, Andrew},
	year = {2016},
	note = {ISSN: 09226389},
	keywords = {Technical Papers: Main Track},
	pages = {230--234}
}
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