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Code and experiment data for the ICAPS 2019 paper "Lagrangian Decomposition for Optimal Cost Partitioning"

Authors: Pommerening, Florian; Röger, Gabriele; Helmert, Malte; Cambazard, Hadrien; Rousseau, Louis-Martin; Salvagnin, Domenico;

Code and experiment data for the ICAPS 2019 paper "Lagrangian Decomposition for Optimal Cost Partitioning"

Abstract

This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. pommerening-et-al-icaps2019-code.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 4.2 for reproducing all experiments of the paper, under experiments/lagrangian. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.) pommerening-et-al-icaps2019-benchmarks.zip contains the benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks). pommerening-et-al-icaps2019-lab.zip contains a copy of Lab 4.2 (https://github.com/aibasel/lab). pommerening-et-al-icaps2019-data.zip contains the experimental data. Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, it contains: the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file - a JSON file with the combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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