Effect-Abstraction Based Relaxation for Linear Numeric Planning

Effect-Abstraction Based Relaxation for Linear Numeric Planning. Li, D., Scala, E., Haslum, P., & Bogomolov, S. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pages 4787–4793, Stockholm, Sweden, July, 2018. International Joint Conferences on Artificial Intelligence Organization.

Paper doi abstract bibtex

This paper studies an effect abstraction-based relaxation for reasoning about linear numeric planning problems. The effect abstraction decomposes non-constant linear numeric effects into actions with conditional, additive constant numeric effects. With little effort, on this abstracted version, it is possible to use known subgoaling-based relaxations and related heuristics. The combination of these two steps leads to a novel relaxation-based heuristic. Theoretically, the relaxation is proved tighter than the previous interval-based relaxation and leading to pruning-safe heuristics. Empirically, a heuristic developed on this relaxation leads to substantial improvements for a class of problems that are currently out of reach of state-of-the-art numeric planners.

@inproceedings{li_effect-abstraction_2018,
	address = {Stockholm, Sweden},
	title = {Effect-{Abstraction} {Based} {Relaxation} for {Linear} {Numeric} {Planning}},
	isbn = {978-0-9992411-2-7},
	url = {https://www.ijcai.org/proceedings/2018/665},
	doi = {10.24963/ijcai.2018/665},
	abstract = {This paper studies an effect abstraction-based relaxation for reasoning about linear numeric planning problems. The effect abstraction decomposes non-constant linear numeric effects into actions with conditional, additive constant numeric effects. With little effort, on this abstracted version, it is possible to use known subgoaling-based relaxations and related heuristics. The combination of these two steps leads to a novel relaxation-based heuristic. Theoretically, the relaxation is proved tighter than the previous interval-based relaxation and leading to pruning-safe heuristics. Empirically, a heuristic developed on this relaxation leads to substantial improvements for a class of problems that are currently out of reach of state-of-the-art numeric planners.},
	language = {en},
	urldate = {2022-12-22},
	booktitle = {Proceedings of the {Twenty}-{Seventh} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	publisher = {International Joint Conferences on Artificial Intelligence Organization},
	author = {Li, Dongxu and Scala, Enrico and Haslum, Patrik and Bogomolov, Sergiy},
	month = jul,
	year = {2018},
	pages = {4787--4793},
}

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