Plan Recognition as Planning

By Miquel Ramírez, Hector Geffner. International Joint Conference on Artificial Intelligence, 2009

359 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9fe1fdf4daaebb4bbd440a5159a05caee0056d79

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Citations in the corpus

Model Recognition as Planning (2019, methodology): “All experiments were run in an Intel Core i5 3.10GHz x 4 16GB of RAM and the classical plan-3 Figure 5: A 4-symbol and 5-state regular automata for recognizing the ( abcd ) + language ( q 4 is the acceptor state). ner we used to solve the instances that result from the compilation was M ADAGASCAR (R”
Interactive and interpretable machine learning models for human machine collaboration (2015, methodology): “Some studies [94, 79] require a library of plans, while others [137, 115, 112, 118] replace this library with relevant structural information.”
A Bayesian model for opening prediction in RTS games with application to StarCraft (2011, methodology): “We used this replay feature to extract players actions and learn the probabilities of tech trees to happen at a given time and, in this paper, also given a labeled opening.”
Probabilistic Plan Recognition Using Off-the-Shelf Classical Planners (2010, background): “In contrast to the transformation introduced in (Ramirez and Geffner 2009), the new transformation does not add new actions, and yet has similar properties.”
Preferred Explanations: Theory and Generation via Planning (2011, background): “These include narrative understanding, plan recognition (Ramı́rez and Geffner 2009), finding excuses (Göbelbecker et al.”
Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, unknown): “(Ramı́rez and Geffner, 2009) no no yes yes no –∗∗ discrete full”