Landmarks for Numeric Planning Problems

Landmarks for Numeric Planning Problems. Scala, E., Haslum, P., Magazzeni, D., & Thiébaux, S. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pages 4384–4390, Melbourne, Australia, August, 2017. International Joint Conferences on Artificial Intelligence Organization.

Paper doi abstract bibtex

The paper generalises the notion of landmarks for reasoning about planning problems involving propositional and numeric variables. Intuitively, numeric landmarks are regions in the metric space deﬁned by the problem whose crossing is necessary for its resolution. The paper proposes a relaxationbased method for their automated extraction directly from the problem structure, and shows how to exploit them to infer what we call disjunctive and additive hybrid action landmarks. The justiﬁcation of such a disjunctive representation results from the intertwined propositional and numeric structure of the problem. The paper exercises their use in two novel admissible LP-Based numeric heuristics, and reports experiments on cost-optimal numeric planning problems. Results show the heuristics are more informed and effective than previous work for problems involving a higher number of (sub)goals.

@inproceedings{scala_landmarks_2017,
	address = {Melbourne, Australia},
	title = {Landmarks for {Numeric} {Planning} {Problems}},
	isbn = {978-0-9992411-0-3},
	url = {https://www.ijcai.org/proceedings/2017/612},
	doi = {10.24963/ijcai.2017/612},
	abstract = {The paper generalises the notion of landmarks for reasoning about planning problems involving propositional and numeric variables. Intuitively, numeric landmarks are regions in the metric space deﬁned by the problem whose crossing is necessary for its resolution. The paper proposes a relaxationbased method for their automated extraction directly from the problem structure, and shows how to exploit them to infer what we call disjunctive and additive hybrid action landmarks. The justiﬁcation of such a disjunctive representation results from the intertwined propositional and numeric structure of the problem. The paper exercises their use in two novel admissible LP-Based numeric heuristics, and reports experiments on cost-optimal numeric planning problems. Results show the heuristics are more informed and effective than previous work for problems involving a higher number of (sub)goals.},
	language = {en},
	urldate = {2023-01-11},
	booktitle = {Proceedings of the {Twenty}-{Sixth} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	publisher = {International Joint Conferences on Artificial Intelligence Organization},
	author = {Scala, Enrico and Haslum, Patrik and Magazzeni, Daniele and Thiébaux, Sylvie},
	month = aug,
	year = {2017},
	pages = {4384--4390},
}

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