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Author, Editor(s)

Author(s):

Hoffmann, Jörg
Brafman, Ronen I.

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dblp

Not MPG Author(s):

Brafman, Ronen I.

BibTeX cite key*:

HoffmannBrafman2006

Title

Title*:

Conformant planning via heuristic forward search: A new approach

Journal

Journal Title*:

Artificial Intelligence

Journal's URL:


Download URL
for the article:

http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6TYF-4J7B133-1-1&_cdi=5617&_user=43521&_orig=search&_coverDate=05%2F31%2F2006&_sk=998299993&view=c&wchp=dGLbVtb-zSkWW&md5=a406e7231282037e956cfefe701508d0&ie=/sdarticle.pdf

Language:

English

Publisher

Publisher's
Name:

Elsevier

Publisher's URL:


Publisher's
Address:

Amsterdam, The Netherlands

ISSN:


Vol, No, pp, Date

Volume*:

170

Number:


Publishing Date:

2006

Pages*:

507-541

Number of
VG Pages:


Page Start:


Page End:


Sequence Number:


DOI:


Note, Abstract, ©

Note:


(LaTeX) Abstract:


Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic forward-search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF's heuristic function, and of FF's machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.

URL for the Abstract:

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TYF-4J7B133-1&_user=43521&_coverDate=05%2F31%2F2006&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000004638&_version=1&_urlVersion=0&_userid=43521&md5=306b0cb83c69e35a94ec9dff1cd5f7a2

Categories,
Keywords:

Planning under uncertainty, Heuristic search planning, Relaxed plan heuristic

HyperLinks / References / URLs:


Copyright Message:


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Access Level:

Intranet

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Programming Logics Group

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BibTeX Entry:

@ARTICLE{HoffmannBrafman2006,
AUTHOR = {Hoffmann, J{\"o}rg and Brafman, Ronen I.},
TITLE = {Conformant planning via heuristic forward search: A new approach},
JOURNAL = {Artificial Intelligence},
PUBLISHER = {Elsevier},
YEAR = {2006},
VOLUME = {170},
PAGES = {507--541},
ADDRESS = {Amsterdam, The Netherlands},
}


Entry last modified by Uwe Brahm, 01/28/2008
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Editor(s)
Jörg Hoffmann
Created
02/13/2006 11:31:49 AM
Revisions
3.
2.
1.
0.
Editor(s)
Uwe Brahm
Uwe Brahm
Christine Kiesel
Jörg Hoffmann
Edit Dates
04/26/2007 01:25:43 AM
04/26/2007 01:25:27 AM
16.02.2007 10:44:53
02/13/2006 11:31:49 AM