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7 results for “PDDL”
PDDL benchmarks for oversubscription planning
<p>Benchmark suite for oversubscription planning.</p> <p>The benchmarks were created in a similar fashion to the procedure described by Domshlak and Mirkis (2015), based on the collection of classical International Planning Competition (IPC) domains. The bounds are set to 25%, 50%, 75%, or 100% of the best known solution cost for the classical planning task obtained from planning.domains (Muise 2016). Each goal in the original problem has a utility of 10 and a randomly chosen 5% of the facts have a uniformly distributed integer utility in the range [1, 5].</p> <p>For more details, see:</p> <p>Michael Katz, Emil Keyder, Florian Pommerening and Dominik Winterer. Oversubscription Planning as Classical Planning with Multiple Cost Functions. In <em>Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS 2019)</em>. 2019.</p>
Experimental data and benchmarks used in the paper "Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks"
<p>This dataset contains both benchmarks and data used in the paper.</p> <p>PDDL benchmark files can be found in the files benchmarks.tar.gz and<br> bagged-benchmarks.tar.gz. The former contains all domains from all IPCs from the<br> repository https://bitbucket.org/aibasel/downward-benchmarks, without duplicate<br> domains that have been used in multiple IPCs. The latter contains the subset of<br> these tasks for which the reformulation in the "bagged representation" from the<br> following paper succeeded:</p> <p>Riddle, P.; Douglas, J.; Barley, M.; and Franco, S. 2016. Improving<br> performance by reformulating PDDL into a bagged representation.<br> In ICAPS 2016 Workshop on Heuristics and Search for Domain-<br> independent Planning, 28–36.</p> <p>We obtained the implementation of the baggy reformluation from the authors.</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.2621897).</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). Some directories (not for<br> the ground experiments, where these files got too large) also contain a file<br> generators.py that contain all symmetry group generators in permutation<br> notation.</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>
Unsolvable PDDL Benchmarks
<p>Collection of unsolvable planning task from (a) the benchmarks used in the Unsolvability IPC 2016 (https://unsolve-ipc.eng.unimelb.edu.au) and (b) http://fai.cs.uni-saarland.de/downloads/unsat-benchmarks.tar.bz2. All solvable problems from the original benchmarks are removed. Some domains and problems overlapped: The problems in the two directories bottleneck and unsat-pegsol-strips from (b) are identical to the ones in bottleneck and pegsol from (a) and thus omitted. We assume that problems in folders unsat-nomystery, unsat-rovers and unsat-tpp from (b) also overlap with those in folders over-nomystery, over-rovers and over-tpp from (a), but kept all problems from those folders.</p>
Grammar2PDDL: PDDL benchmark generated from data science grammar
<p>A PDDL benchmark set, generated from Data Science grammar by requiring particular rules and terminals as soft goals.</p> <p>The dataset can also be found at <a href="https://github.com/IBM/PDDL-benchmark-ds-grammar">https://github.com/IBM/PDDL-benchmark-ds-grammar</a></p>
STRIPS PDDL benchmarks from sequential optimization tracks of IPC 1998-2014
<p>Subset of STRIPS benchmarks from the sequential optimization tracks of IPC 1998-2014 from <a href="https://github.com/aibasel/downward-benchmarks">https://github.com/aibasel/downward-benchmarks</a>.</p>
STRIPS PDDL benchmarks from sequential optimization tracks of IPC 1998-2018
<p>Subset of STRIPS benchmarks from the sequential optimization tracks of IPC 1998-2018 from <a href="github.com/aibasel/downward-benchmarks">https://github.com/aibasel/downward-benchmarks</a>.</p>
PDDL+ Dataset
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