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Iterative Bounding LAO*
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology. (APD)
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. (APD)ORCID iD: 0000-0002-5500-8494
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
2010 (English)In: ECAI 2010: 19th European Conference on Artificial Intelligence - Volume 215 Frontiers in Artificial Intelligence and Applications / [ed] Helder Coelho, Rudi Studer and Mike Wooldridge, IOS Press , 2010, p. 341-346Conference paper, Published paper (Refereed)
Abstract [en]

Iterative Bounding LAO* is a new algorithm for epsilon- optimal probabilistic planning problems where an absorbing goal state should be reached at a minimum expected cost from a given initial state. The algorithm is based on the LAO* algorithm for finding optimal solutions in cyclic AND/OR graphs. The new algorithm uses two heuristics, one upper bound and one lower bound of the optimal cost. The search is guided by the lower bound as in LAO*, while the upper bound is used to prune search branches. The algorithm has a new mechanism for expanding search nodes, and while maintaining the error bounds, it may use weighted heuristics to reduce the size of the explored search space. In empirical tests on benchmark problems, Iterative Bounding LAO* expands fewer search nodes compared to state of the art RTDP variants that also use two-sided bounds.

Place, publisher, year, edition, pages
IOS Press , 2010. p. 341-346
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 215
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-59883DOI: 10.3233/978-1-60750-606-5-341ISBN: 978-1-60750-605-8 (print)ISBN: 978-1-60750-606-5 (print)OAI: oai:DiVA.org:liu-59883DiVA, id: diva2:353971
Conference
The 19th European Conference on Artificial Intelligence (ECAI), August 16-20, Lisbon, Portugal
Available from: 2010-09-29 Created: 2010-09-29 Last updated: 2018-01-12Bibliographically approved
In thesis
1. Troubleshooting Trucks: Automated Planning and Diagnosis
Open this publication in new window or tab >>Troubleshooting Trucks: Automated Planning and Diagnosis
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Felsökning av lastbilar : automatiserad planering och diagnos
Abstract [en]

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken.

The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time.

The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner.

The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. p. 79
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1691
Keywords
automated planning, diagnosis, troubleshooting, automotive systems, Bayesian networks, Markov decision-processes
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-119445 (URN)10.3384/diss.diva-119445 (DOI)978-91-7685-993-3 (ISBN)
Public defence
2015-10-16, Visionen, Hus B, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Funder
Vinnova, 2010-02864
Available from: 2015-09-23 Created: 2015-06-17 Last updated: 2019-11-15Bibliographically approved

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Warnquist, HåkanKvarnström, JonasDoherty, Patrick

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