Endomorphisms of Classical Planning Tasks

Authors: Rostislav Horčík, Daniel Fišer11835-11843

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Finally, we experimentally verify that the proposed method can find a sizable number of redundant operators on the standard benchmark set.
Researcher Affiliation Academia 1 Czech Technical University in Prague, Faculty of Electrical Engineering, Prague, Czech Republic 2 Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany
Pseudocode No The paper describes methods and definitions but does not include any structured pseudocode or algorithm blocks.
Open Source Code Yes The inference of endomorphisms and pruning of redundant operators was implemented3 in C and experimentally evaluated on a cluster of computing nodes with Intel Xeon Scalable Gold 6146 processors. 3https://gitlab.com/danfis/cpddl, branch aaai21-endomorphism
Open Datasets Yes We used all planning domains from the optimal tracks of International Planning Competitions (IPCs) from 1998 to 2018 excluding the ones containing conditional effects after translation (leaving 65 domains).
Dataset Splits No The paper mentions using planning domains from the International Planning Competitions (IPCs) but does not provide specific details on how the datasets were split into training, validation, or test sets.
Hardware Specification Yes The inference of endomorphisms and pruning of redundant operators was implemented3 in C and experimentally evaluated on a cluster of computing nodes with Intel Xeon Scalable Gold 6146 processors.
Software Dependencies Yes For solving CSPs, we used CP Optimizer from IBM ILOG CPLEX Optimization Studio v12.9.
Experiment Setup Yes Table 1 shows the results where the time limit for the inference of endomorphisms was set to 90 seconds and the memory limit was set to 16 GB.