@article {18921,
	title = {Task decomposition on abstract states, for planning under nondeterminism},
	journal = {Artificial Intelligence},
	volume = {173},
	year = {2009},
	month = {2009/04//},
	pages = {669 - 695},
	abstract = {Although several approaches have been developed for planning in nondeterministic domains, solving large planning problems is still quite difficult. In this work, we present a new planning algorithm, called Yoyo, for solving planning problems in fully observable nondeterministic domains. Yoyo combines an HTN-based mechanism for constraining its search and a Binary Decision Diagram (BDD) representation for reasoning about sets of states and state transitions.We provide correctness theorems for Yoyo, and an experimental comparison of it with MBP and ND-SHOP2, the two previously-best algorithms for planning in nondeterministic domains. In our experiments, Yoyo could easily deal with problem sizes that neither MBP nor ND-SHOP2 could scale up to, and could solve problems about 100 to 1000 times faster than MBP and ND-SHOP2.
},
	keywords = {Binary decision diagrams, Hierarchical task-network (HTN) planning, Planning in nondeterministic domains},
	isbn = {0004-3702},
	doi = {10.1016/j.artint.2008.11.012},
	url = {http://www.sciencedirect.com/science/article/pii/S0004370208001987},
	author = {Kuter,Ugur and Nau, Dana S. and Pistore,Marco and Traverso,Paolo}
}
