FF: The Fast-Forward Planning System

By Jörg Hoffmann. The AI Magazine, 2001

386 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Large Language Models as Commonsense Knowledge for Large-Scale Task Planning (2023, background): “In the early stage, many studies [1, 15, 14, 12] focused on task planning in a discrete state space with deterministic transitions.”
Lifelong Planning A (2004, background): “0 [16], FF [17], GRT [18], and AltAlt [19].”
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models (2022, methodology): “Heuristics are often used to guide the search [10], [11], [12], [13].”
Learning Generalized Reactive Policies using Deep Neural Networks (2017, neutral): “n off-the-shelf planner to generate a plan and corresponding trajectory ˝, and then add the observations and actions in ˝to D imitation. In our experiments we used the Fast-Forward (FF) planner (Jörg Hoffman 2001), though any other PDDL planner can be used instead. Policy training Given the generate”
Automated Planning and Acting (2016, methodology): “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.”