A Concise Introduction to Models and Methods for Automated Planning

By Hector Geffner, Blai Bonet. 2013

412 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, background): “It then produces symbolic PDDL-like planning representations that account for the input graphs in the form of a general ﬁrst-order domain with action schemas and predicate symbols, and instance information describing objects and an initial situation.”
Learning Features and Abstract Actions for Computing Generalized Plans (2018, positive): “The abstract actions provide a model from which the plans are obtained via transformations and FOND planners (Geffner and Bonet 2013; Ghallab, Nau, and Traverso 2016).”
Blind Search for Atari-Like Online Planning Revisited (2016, methodology): “The implicit, simulator-based problem representation in ALE precludes automatic derivation of heuristic functions and other inferences developed in the scope of classical planning [Ghallab et al., 2004; Geffner and Bonet, 2013].”
Rearrangement: A Challenge for Embodied AI (2020, background): “A number of books and surveys cover this body of work [30, 32, 40].”
Model-free, Model-based, and General Intelligence (2018, background): “stochastic planning problems (FONDPs, PONDPs, MDPs, and POMDPs), general game playing, and answer set programming among others [Dechter, 2003; Biere et al., 2009; Pearl, 1988; Geffner and Bonet, 2013; Bertsekas, 1995; Kaelbling et al., 1998; Genesereth et al., 2005; Gebser et al., 2012].”
Combining Planning with Gaze for Online Human Intention Recognition (2018, methodology): “In this model, we assume a STRIPS-like model of ontic actions [10], with each action consisting of a unique name, preconditions that determine under which worlds inW they can be executed, and effects that describe the changes made toW .”
Deliberation for autonomous robots: A survey (2017, background): “A survey of task planning is not the purpose of this section (see [86, 91, 222, 234]).”
Classical Planning with Simulators: Results on the Atari Video Games (2015, background): “While all these games feature a known initial (RAM) state and actions that have deterministic effects, the problem of selecting the next action to be done cannot be addressed with off-the-shelf classical planners [Ghallab et al., 2004; Geffner and Bonet, 2013].”
A review of generalized planning (2019, background): “Off-line algorithms for contingent [1], conformant [69] and POMDP planning [32] can also be understood as top-down algorithms for generalized planning if we consider that the possible initial states represent different planning instances all sharing the same goals.”