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12 results for “classical planning”
Data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>This data set contains raw data and parsed data of all experiments [1] run for the PhD thesis. They were generated using lab (see https://doi.org/10.5281/zenodo.399255).</p> <p>The raw data files (sievers-phd2017-raw-data-part*.tar.gz) contain a subdirectory for each experiment, each containing a subdirectory for each planner run of the experiment, distributed over the directories runs-*. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), and the run script "run" used to start the experiment. The latter cannot be directly used, however, because the directory containing source code and build (compiled object files) have been removed for space reasons. The code is publicly available under https://doi.org/10.5281/zenodo.1163381. The (lab) scripts for parsing run.log are also available in the main directory of each experiment. All other scripts and a corresponding lab version are available on request.</p> <p>For each raw data experiment, the parsed data file (sievers-phd2017-parsed-data.tar.gz) also contains a directory of the same name, with "-eval" appended. It contains a single file called "properties" that combines all of the experiment's parsed data (which can be and was generated from the raw data using lab and the parser scripts). They are in the json format and can be used for easy manipulation of the data. The directories with the prefix "paper-" and "talk-" are combinations of other directories (using the "fetch" mechanism of lab). It is recommended to use these, because due to technical errors, the original eval directories do not contain all runs of all planners (to be more precise: they contain all runs, but a subset of the planner have not been started in these experiments for technical errors and thus considered not solving the task). The missing ones have been run separately, see the directories with "missing-runs" in their name. This is also the reason some of these directories ("paper-", "talk-") contain files named "old-properties" and "fixed-properties" besides the actual "properties". "old-properties" are those with missing/faulty runs, "fixed-properties" are as "old-properties", however with the data of faulty runs removed, and "properties" are as "fixed-properties", however with the addition of the fixed missing runs (in fact, these always contain *all* fixed missing runs of all experiments, for technical reasons).</p> <p>The file sievers-phd2017-parsed-data-all-and-random-merge-strategies.tar.gz contains parsed data of earlier experiments (see [1]), for which no raw data has been archived. The directories contain properties files in the json format.</p> <p>[1] except raw data for the parsed data "sota-symba-spmas-eval" (which in the meantime was added to a separate data set available under https://doi.org/10.5281/zenodo.1189912) and all re-used experiments from the paper "An Analysis of Merge Strategies for Merge-and-Shrink Heuristics" (Silvan Sievers, Martin Wehrle and Malte Helmert, ICAPS 2016), for which the raw data was too large to be archived.</p>
Dataset used in the paper "Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions"
<p>This dataset contains all raw and processed data used in the paper. It has been generated using Downward-Lab (see https://doi.org/10.5281/zenodo.399255).</p> <p>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, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), 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).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with 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.</p> <p>To process the data further, we used the scripts available in the software bundle of the paper: https://doi.org/10.5281/zenodo.1290524</p>
Additional data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>The original data set for the thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation" by Sievers, 2017, available under https://doi.org/10.5281/zenodo.1164137, accidentally did not include the data of one experiment, namely the raw data of the parsed data contained in "sota-symba-spmas-eval". It can be found in this data set.</p>
Experimental data from the PhD thesis "Counterexample-guided Cartesian Abstraction Refinement and Saturated Cost Partitioning for Optimal Classical Planning"
<p>The three data sets contain the raw experiment data, parsed values and basic reports for the three parts of the thesis. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains "properties" file with all parsed values and an HTML report.</p>
Experimental data of the paper "Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning"
<p>This dataset contains experimental data used in the paper. They were generated<br> using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). The scripts<br> used to run the experiments can be found in the software bundle for this paper<br> (see https://doi.org/10.5281/zenodo.2628703).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Experimental data and benchmarks of the paper "Merge-and-Shrink Task Reformulation for Classical Planning"
<p>This dataset contains both benchmarks and data used in the paper.</p> <p>PDDL benchmark files can be found in the file optimal-strips-benchmarks.tar.gz<br> and satisficing-strips-benchmarks.tar.gz, which contain the STRIPS domains from<br> the sequential optimal/satisficing tracks of all IPCs as collected in the<br> repository https://bitbucket.org/aibasel/downward-benchmarks.</p> <p>All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.3232878).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Data for domains without generator for the ICAPS 2021 paper "Automatic Instance Generation for Classical Planning"'
<p>This dataset contains raw data (log files) and parsed data (JSON files) of all planners used in the paper run on planning domains for which there is no generator that could directly be used for the Autoscale training process. This dataset was used to select a subset of tasks as described in the paper, for all Autoscale versions up to this point (Autoscale 21.08 and 21.11). As such, it complements the original Zenodo entry https://zenodo.org/record/4586397.</p> <p>domains-without-generator.zip contains the raw experimental 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, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), 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).</p> <p>domains-without-generator-eval.zip contains the parsed data, again distributed over a subdirectory for each experiment. Each contains a "properties" file, which is a JSON file with 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.</p>
Experimental data from the paper "Counterexample-guided Cartesian Abstraction Refinement for Classical Planning"
<p>The data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Experimental data from the paper "Subset-Saturated Cost Partitioning for Optimal Classical Planning"
<p>The data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Experimental data from the IJCAI 2019 paper "Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning"
<p>This data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and to save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Experimental data from the JAIR 2020 paper "Saturated Cost Partitioning for Optimal Classical Planning"
<p>This data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and to save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
The Effect of Digital and Classical Education Based on Planned Behavior Theory Given to Mothers in the Risk Group on Their Self-Efficacy in Identifying Safety Measures and First Aid for Home Accidents
ClinicalTrials.gov study NCT06981416. IPD Sharing: NO. Countries: 1. Publications: 0.
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