LiU Electronic Press
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Author:
Nilsson, Mikael (Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems) (Linköping University, The Institute of Technology) (KPLAB)
Kvarnström, Jonas (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology) (APD)
Doherty, Patrick (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies) (Linköping University, The Institute of Technology)
Title:
Incremental Dynamic Controllability Revisited
Department:
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Linköping University, Department of Computer and Information Science, UASTECH - Autonomous Unmanned Aircraft Systems Technologies
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS)
Conference:
23rd International Conference on Automated Planning and Scheduling (ICAPS 2013), 10-14 June 2013, Rom, Italy
Publisher: AAAI Press
Year of publ.:
2013
URI:
urn:nbn:se:liu:diva-88634
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-88634
ISBN:
978-1-57735-609-7
Subject category:
Computer Science
SVEP category:
Computer science
Project:
CUAS
Abstract(en) :

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems where some durations are determined by nature, as is often the case for actions in planning. As such networks are generated it is essential to verify that they are dynamically controllable – executable regardless of the outcomes of uncontrollable durations – and to convert them to a dispatchable form. The previously published FastIDC algorithm achieves this incrementally and can therefore be used efficiently during plan construction. In this paper we show that FastIDC is not sound when new constraints are added, sometimes labeling networks as dynamically controllable when they are not. We analyze the algorithm, pinpoint the cause, and show how the algorithm can be modified to correctly detect uncontrollable networks.

Available from:
2013-02-14
Created:
2013-02-14
Last updated:
2014-03-26
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