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Employing a Restricted Set of Qualitative Relations in Recognizing Plain Sketches Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Interval Based Relaxation Heuristics for Numeric Planning with Action Costs

Authors : Johannes Aldinger, Bernhard Nebel

Published in: KI 2017: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Many real-world problems can be expressed in terms of states and actions that modify the world to reach a certain goal. Such problems can be solved by automated planning. Numeric planning supports numeric quantities such as resources or physical properties in addition to the propositional variables from classical planning. We approach numeric planning with heuristic search and introduce adaptations of the relaxation heuristics \(h_\text {max}\), \(h_\text {add}\) and \(h_{\text {FF}}\) to interval based relaxation frameworks. In contrast to previous approaches, the heuristics presented in this paper are not limited to fragments of numeric planning with instantaneous actions (such as linear or acyclic numeric planning tasks) and support action costs.

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Footnotes
1
Scala et al. [17] run into a similar problem using “asynchronous subgoaling” and have to set the cost of hard conditions to 0 to ensure admissibility of \(h_\text {max}\).
 
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Metadata
Title
Interval Based Relaxation Heuristics for Numeric Planning with Action Costs
Authors
Johannes Aldinger
Bernhard Nebel
Copyright Year
2017
Book
KI 2017: Advances in Artificial Intelligence

Print ISBN: 978-3-319-67189-5

Electronic ISBN: 978-3-319-67190-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-67190-1_2

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