Non-deterministic planning with numeric uncertainty

Liana Marinescu; Andrew Coles

doi:10.3233/978-1-61499-672-9-1694

Non-deterministic planning with numeric uncertainty

Liana Marinescu, Andrew Coles

Department of Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

1 Citation (Scopus)

132 Downloads (Pure)

Abstract

Uncertainty arises in many compelling real-world applications of planning. There is a large body of work on propositional uncertainty where actions have non-deterministic outcomes. However handling numeric uncertainty has been given less consideration. In this paper, we present a novel offline policy-building approach for problems with numeric uncertainty. In particular, inspired by the planner PRP, we define a numeric constraint representation that captures only relevant numeric information, supporting a more compact policy representation. We also show how numeric dead ends can be generalised to avoid redundant search. Empirical results show we can substantially reduce the time taken to build a policy.

Original language	English
Title of host publication	Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016)
Publisher	IOS Press
Pages	1694-1695
Number of pages	2
Volume	285
ISBN (Print)	9781614996712
DOIs	https://doi.org/10.3233/978-1-61499-672-9-1694
Publication status	Published - 2016
Event	22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands Duration: 29 Aug 2016 → 2 Sept 2016

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	285
ISSN (Print)	09226389

Conference

Conference	22nd European Conference on Artificial Intelligence, ECAI 2016
Country/Territory	Netherlands
City	The Hague
Period	29/08/2016 → 2/09/2016

Access to Document

10.3233/978-1-61499-672-9-1694Licence: CC BY

Non-Deterministic Planning_MARINESCU_Accepted2016_GOLD VoR (CC BY)Final published version, 165 KBLicence: CC BY

Cite this

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title = "Non-deterministic planning with numeric uncertainty",

abstract = "Uncertainty arises in many compelling real-world applications of planning. There is a large body of work on propositional uncertainty where actions have non-deterministic outcomes. However handling numeric uncertainty has been given less consideration. In this paper, we present a novel offline policy-building approach for problems with numeric uncertainty. In particular, inspired by the planner PRP, we define a numeric constraint representation that captures only relevant numeric information, supporting a more compact policy representation. We also show how numeric dead ends can be generalised to avoid redundant search. Empirical results show we can substantially reduce the time taken to build a policy.",

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Marinescu, L & Coles, A 2016, Non-deterministic planning with numeric uncertainty. in Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016). vol. 285, Frontiers in Artificial Intelligence and Applications, vol. 285, IOS Press, pp. 1694-1695, 22nd European Conference on Artificial Intelligence, ECAI 2016, The Hague, Netherlands, 29/08/2016. https://doi.org/10.3233/978-1-61499-672-9-1694

Non-deterministic planning with numeric uncertainty. / Marinescu, Liana ; Coles, Andrew.
Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016). Vol. 285 IOS Press, 2016. p. 1694-1695 (Frontiers in Artificial Intelligence and Applications; Vol. 285).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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AU - Coles, Andrew

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N2 - Uncertainty arises in many compelling real-world applications of planning. There is a large body of work on propositional uncertainty where actions have non-deterministic outcomes. However handling numeric uncertainty has been given less consideration. In this paper, we present a novel offline policy-building approach for problems with numeric uncertainty. In particular, inspired by the planner PRP, we define a numeric constraint representation that captures only relevant numeric information, supporting a more compact policy representation. We also show how numeric dead ends can be generalised to avoid redundant search. Empirical results show we can substantially reduce the time taken to build a policy.

AB - Uncertainty arises in many compelling real-world applications of planning. There is a large body of work on propositional uncertainty where actions have non-deterministic outcomes. However handling numeric uncertainty has been given less consideration. In this paper, we present a novel offline policy-building approach for problems with numeric uncertainty. In particular, inspired by the planner PRP, we define a numeric constraint representation that captures only relevant numeric information, supporting a more compact policy representation. We also show how numeric dead ends can be generalised to avoid redundant search. Empirical results show we can substantially reduce the time taken to build a policy.

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T3 - Frontiers in Artificial Intelligence and Applications

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BT - Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016)

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T2 - 22nd European Conference on Artificial Intelligence, ECAI 2016

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ER -

Non-deterministic planning with numeric uncertainty

Abstract

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Heuristic Guidance for Forward-Chaining Planning with Numeric Uncertainty

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