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Strong planning under partial observability
Artificial Intelligence 170 (4-5):337-384 (2006)
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Citations of this work
Knowledge-based programs as succinct policies for partially observable domains.Bruno Zanuttini, Jérôme Lang, Abdallah Saffidine & François Schwarzentruber - 2020 - Artificial Intelligence 288 (C):103365.
Analyzing generalized planning under nondeterminism.Vaishak Belle - 2022 - Artificial Intelligence 307 (C):103696.
Conformant plans and beyond: Principles and complexity.Blai Bonet - 2010 - Artificial Intelligence 174 (3-4):245-269.
Task decomposition on abstract states, for planning under nondeterminism.Ugur Kuter, Dana Nau, Marco Pistore & Paolo Traverso - 2009 - Artificial Intelligence 173 (5-6):669-695.
Plan aggregation for strong cyclic planning in nondeterministic domains.Ron Alford, Ugur Kuter, Dana Nau & Robert P. Goldman - 2014 - Artificial Intelligence 216 (C):206-232.
References found in this work
Planning and acting in partially observable stochastic domains.Leslie Pack Kaelbling, Michael L. Littman & Anthony R. Cassandra - 1998 - Artificial Intelligence 101 (1-2):99-134.
Using temporal logics to express search control knowledge for planning.Fahiem Bacchus & Froduald Kabanza - 2000 - Artificial Intelligence 116 (1-2):123-191.
Weak, strong, and strong cyclic planning via symbolic model checking.A. Cimatti, M. Pistore, M. Roveri & P. Traverso - 2003 - Artificial Intelligence 147 (1-2):35-84.
An algorithm for probabilistic planning.Nicholas Kushmerick, Steve Hanks & Daniel S. Weld - 1995 - Artificial Intelligence 76 (1-2):239-286.
LAO∗: A heuristic search algorithm that finds solutions with loops.Eric A. Hansen & Shlomo Zilberstein - 2001 - Artificial Intelligence 129 (1-2):35-62.
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