Planning with Incomplete Information as Heuristic Search in Belief Space

By Blai Bonet, Hector Geffner. International Conference on Artificial Intelligence Planning Systems, 2000

403 citations recorded by the source corpus.

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Citations in the corpus

Automated composition of Web services via planning in asynchronous domains (2005, background): “[18,81,93,24], we remark again that their use of beliefs is different, due to the absence of asynchronicity in their models.”
Automated Composition of Semantic Web Services into Executable Processes (2004, unknown): “Such a set of states is called a belief state (or simply belief ) [8, 6].”
A new representation and associated algorithms for generalized planning (2011, methodology): “states represent sets of possible concrete states in a manner similar to the modelling approach used in contingent planning, where belief states [3,16] represent sets of possible real world states which are indistinguishable due to lack of information.”
Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming (2003, methodology): “RTDP corresponds to a generalization of Korf's LRTA* to non-deterministic settings and is the algorithm that underlies the GPT planner (Bonet & Geffner 2000; 2001a).”
An Algorithm Better than AO*? (2005, background): “In the last few years, however, there has been a renewed interest in the AO* algorithm in planning research where problems involving uncertainty and feedback can be formulated as search problems over AND/OR graphs (Bonet & Geffner 2000).”
A Translation-Based Approach to Contingent Planning (2009, background): “In the last few years, significant progress has been achieved resulting in a variety of contingent planners that can solve large and non-trivial problems, usually by casting the contingent planning problem as a non-deterministic (AND/OR) search problem in belief space [Bonet and Geffner, 2000; Hoffm”
Combined task and motion planning through an extensible planner-independent interface layer (2014, background): “An alternate representation for dealing with large sets of relevant facts in the initial state would be to treat them as initially unknown and use a partially observable planner with non-deterministic “sensing” actions [18].”
Weak, strong, and strong cyclic planning via symbolic model checking (2003, methodology): “For instance, for MBP, SIMPLAN and UMOP, the preprocessing time is related to the domain (the internal representation resulting from the preprocessing can be reused for several planning problems), while for GPT and QBFPLAN the overhead is problem specific, and should be taken into account for each p”
Automated Planning and Acting (2016, background): “Among these are Hector Geffner, Robert Goldman, Patrik Haslum, Jörg Hoffmann, Joachim Hertzberg, Felix Ingrand, Ugur Kuter, Marco Pistore, Mak Roberts, Vikas Shivashankar, Sylvie Thiébaux, and Qiang Yang.”