Intelligent belief state sampling for conformant planning
Date
2017
Authors
Grastien, Alban
Scala, Enrico
Journal Title
Journal ISSN
Volume Title
Publisher
International Joint Conferences on Artificial Intelligence
Abstract
We propose a new method for conformant planning
based on two ideas. First given a small sample of
the initial belief state we reduce conformant planning
for this sample to a classical planning problem,
giving us a candidate solution. Second we
exploit regression as a way to compactly represent
necessary conditions for such a solution to be
valid for the non-deterministic setting. If necessary,
we use the resulting formula to extract a counterexample
to populate our next sampling. Our experiments
show that this approach is competitive on a
class of problems that are hard for traditional planners,
and also returns generally shorter plans. We
are also able to demonstrate unsatisfiability of some
problems.
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Source
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
Type
Conference paper
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Restricted until
2099-12-31