The Metric-FF Planning System: Translating ''Ignoring Delete Lists'' to Numeric State Variables

By Jörg Hoffmann. Journal of Artificial Intelligence Research, 2003

530 citations recorded by the source corpus.

Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). Currently one of the most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the heuristic is based on relaxing the planning task by ignoring the delete lists of the available actions. We present a natural extension of "ignoring delete lists" to numeric state variables, preserving the relevant theoretical properties of the STRIPS relaxation under the condition that the numeric task at hand is "monotonic". We then identify a subset of the numeric IPC-3 competition language, "linear tasks", where monotonicity can be achieved by pre-processing. Based on that, we extend the algorithms used in the heuristic planning system FF to linear tasks. The resulting system Metric-FF is, according to the IPC-3 results which we discuss, one of the two currently most efficient numeric planners.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/0c2984cb646ddc5ce4879fa7b54ff9b7cc37b18f

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Citations in the corpus

What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning (2019, methodology): “In this work we use the Metric-FF PDDL solver [16], one of the most popular planning algorithms for operating on PDDL instances.”
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning (2018, background): “Most numeric planners are limited to problems with linear or polynomial dynamics (Hoffmann 2003; Bryce et al. 2015; Cashmore et al. 2016); however, some planners can handle non-polynomial dynamics by discretizing time (Della Penna et al.”
Semantic Web Service Composition Planning with OWLS-Xplan (2005, background): “In most cases these functions are not only bounded in their number but rather hard-coded in the planner such as in the Metric-FF planner (Hoffmann 2003).”
The 3rd International Planning Competition: Results and Analysis (2003, methodology): “The analysis we performed in addressing the first of these issues is a statistical complement to the theoretical analysis of domain topologies being carried out by Hoffmann (2003b).”
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.”