Higher-Dimensional Potential Heuristics for Optimal Classical Planning

Authors: Florian Pommerening, Malte Helmert, Blai Bonet

AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We implemented oneand two-dimensional admissible potential heuristics (called hpot 1 and hpot 2 in the following) in the Fast Downward planning system (Helmert 2006) and evaluated them on the tasks from the optimal tracks of IPC 1998 2014 using limits of 2 GB and 24 hours for memory and run time.
Researcher Affiliation Academia Florian Pommerening, Malte Helmert University of Basel, Switzerland {florian.pommerening, malte.helmert}@unibas.ch Blai Bonet Universidad Simón Bolívar, Venezuela bonet@ldc.usb.ve
Pseudocode No The paper describes algorithms but does not contain structured pseudocode or algorithm blocks with clear labels like 'Algorithm' or 'Pseudocode'.
Open Source Code No The paper does not contain any explicit statement about releasing the source code for the methodology described, nor does it provide a link to a code repository.
Open Datasets Yes We implemented oneand two-dimensional admissible potential heuristics (called hpot 1 and hpot 2 in the following) in the Fast Downward planning system (Helmert 2006) and evaluated them on the tasks from the optimal tracks of IPC 1998 2014 using limits of 2 GB and 24 hours for memory and run time.
Dataset Splits No The paper evaluates on 'tasks from the optimal tracks of IPC 1998-2014' but does not specify any dataset splits (e.g., percentages, sample counts, or explicit validation set usage) needed for reproduction in the context of model training.
Hardware Specification No The paper mentions 'limits of 2 GB and 24 hours for memory and run time' but does not provide specific hardware details like CPU/GPU models or memory amounts used for running experiments.
Software Dependencies No The paper mentions implementing in the 'Fast Downward planning system (Helmert 2006)', but it does not specify version numbers for any ancillary software dependencies or libraries.
Experiment Setup No The paper mentions 'limits of 2 GB and 24 hours for memory and run time' but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings.