Automated Planning: Theory & Practice

By Dana S. Nau, Malik Ghallab, Paolo Traverso. 2004

1,059 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b833f7cac976a3935c32a72e3ab171cd54e36a15

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Citations in the corpus

Human-aware task planning for mobile robots (2009, background): “Classical AI planning systems, in which the state of the world is only affected by the actions of the robot [24], are no longer applicable.”
Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences (2014, methodology): “In this, section we additionally assume that we have access to the set of feasible alternatives to the observed behavior for example, by assuming access to the planning problem the user faced and building upon the work in automated planning [Nau et al. 2004].”
On Learning Sparse Boolean Formulae for Explaining AI Decisions (2017, background): “Even though the decisions of intelligent agents are the consequence of algorithmic processing of perceived system and environment states [31, 25], the straight-forward approach of reviewing this processing is not practical.”
Physical Causality of Action Verbs in Grounded Language Understanding (2016, background): “Based on action schemas, planning algorithms can be applied to find a sequence of actions to achieve a goal state (Ghallab et al., 2004).”
A review of learning planning action models (2018, background): “In cases of faulty calibration, the state of the system is only partially observable at run time, thus observations return a set of states (called as “belief states”) instead of a single state (Ghallab et al. (2004)).”
Robot task planning using semantic maps (2008, background): “Most task planners maintain an internal state, and compute how this state would change when different actions are applied [35].”