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Optimal temporal planning using the plangraph framework

Dinh, Tien Ba (2007) Optimal temporal planning using the plangraph framework. Doctoral thesis, University of Huddersfield.

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    Abstract

    The past few years have seen a rapid development in AI Planning and Scheduling. Many
    algorithms and techniques have been studied and improved to deal with more complex and
    difficult planning domains.
    One such innovation was Graphplan, first developed by Blum and Furst in 1995 and soon
    became one of the best approaches for optimal classical planning systems. Planning systems
    that use Graphplan’s plangraph framework can find optimal plans for temporal planning
    problems, in which actions have durations. However, these systems have had strict
    assumptions on the preconditions and effects of actions, for instance, effects happen only
    at the end of the execution. In addition, the algorithm used in the solution extraction phase
    of these plangraph-based systems does not take full advantage of the information provided
    by the expansion phase to prune irrelevant search branches early.
    With the ambition to make temporal planning problems more realistic, the thesis proposes
    an extension to the Planning Domain Definition Language (PDDL) 2.1 level 3, to allow
    actions to have intermediate effects. Our optimal temporal planning system, CPPlanner,
    is introduced as the first Graphplan-based optimal planner to handle the richer temporal
    domains (i.e. actions can have intermediate effects). Futhermore, the planner applies “critical
    paths” as a backbone for the search in the solution extraction phase, so that irrelevant
    search branches are pruned early. This improves the performance even in more restricted
    temporal planning domains.
    In our experimental evaluation, CPPlanner outperforms two leading plangraph-based optimal
    temporal planning systems, TGP and TPSYS, in almost all test cases. The state-of-theart
    optimal planner CPT and latest temporal planning domains in the international planning
    competition in 2004 and 2006 are also used in the experimental evaluation.

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    Item Type: Thesis (Doctoral)
    Additional Information: © Tien Ba Dinh
    Uncontrolled Keywords: ai artificial intelligence planning scheduling systems plangraph framework temporal
    Subjects: Q Science > Q Science (General)
    Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Schools: School of Computing and Engineering
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    Depositing User: Sara Taylor
    Date Deposited: 20 Dec 2007
    Last Modified: 28 Jul 2010 19:20
    URI: http://eprints.hud.ac.uk/id/eprint/250

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