English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

Fast Probabilistic Planning Through Weighted Model Counting

MPS-Authors
/persons/resource/persons44632

Hoffmann,  Jörg
Programming Logics, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Domshlak, C., & Hoffmann, J. (2006). Fast Probabilistic Planning Through Weighted Model Counting. In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling (ICAPS 2006) (pp. 243-252). Menlo Park, USA: AAAI.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-22CE-0
Abstract
We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FF's techniques with a powerful machinery for weighted model counting in (weighted) CNFs, serving to elegantly define both the search space and the heuristic function. Our evaluation of Probabilistic-FF on several probabilistic domains shows an unprecedented, several orders of magnitude improvement over previous results in this area.