FF-Replan: A Baseline for Probabilistic Planning

By Sung Wook Yoon, Alan Fern, Robert Givan. International Conference on Automated Planning and Scheduling, 2007

355 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5d361c5ac37a016c34051bb15802ee2102d49a80

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Citations in the corpus

Combined task and motion planning through an extensible planner-independent interface layer (2014, background): “Reinvoking task planners relates to replanning for partially observable or non-deterministic environments [16], [17].”
Belief space planning assuming maximum likelihood observations (2010, unknown): “This approach, as embodied in FF-Replan, has been very successful (won the ICAPS06 planning competition) in a variety of contexts [9].”
Discovering Relational Domain Features for Probabilistic Planning (2007, result): “Our learner also demonstrates superior ability to learn value functions in the planning domains comparing to the evaluation in the latest RRL work (Driessens, Ramon, & G¨ artner 2006), and shows generally superior performance in success ratio comparing to state-of-the-art probabilistic planners FF-R”
Automated Planning and Acting (2016, methodology): “[619] in the context of the International Planning Competition.”
Online Replanning in Belief Space for Partially Observable Task and Motion Problems (2019, methodology): “Another online planning strategy is to approximate the original stochastic problem as a deterministic problem through the process of determinization [9], [22], [10].”
DiscoverHistory: understanding the past in planning and execution (2012, background): “These works focus only on execution-time failures as discrepancies; that is, an unexpected state observed during execution triggers a re-planning or plan repair process [24, 9, 1, 22, 21, 18, 17].”
Integrated task and motion planning in belief space (2013, methodology): “This method has worked well in control applications (Mayne and Michalska, 1990) as well as symbolic planning domains (Yoon et al., 2007).”
Shared autonomy via hindsight optimization for teleoperation and teaming (2017, background): “Hindsight optimization has demonstrated eﬀectiveness in other domains (Yoon et al., 2007, 2008).”