Grid Pathfinding on the 2kNeighborhoods

Authors: Nicolas Rivera, Carlos Hern‡ndez, Jorge Baier

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

Reproducibility Variable Result LLM Response
Research Type Experimental Our empirical evaluation shows that the heuristics we propose are superior to the Euclidean distance (ED) when regular A* is used.
Researcher Affiliation Academia Nicol as Rivera Department of Informatics King s College London London, UK Carlos Hern andez Depto. de Ciencias de la Ingenier ıa Universidad Andr es Bello Concepci on, Chile Jorge A. Baier Pontificia Universidad Cat olica de Chile Center for the Semantic Web Research Santiago, Chile
Pseudocode Yes Algorithm 1: Heuristics for the 8-, 16-, and 32neighborhoods
Open Source Code No Finally, we implemented 2k-neighborhood on top of the regular A* code used in (Uras and Koenig 2015) (Code available at: http://idm-lab.org/anyangle). The paper does not state that the authors' own implementation of the 2k-neighborhood is open-sourced or provide a direct link to it.
Open Datasets Yes We used maps from the Moving AI repository (Sturtevant 2012).
Dataset Splits No For each Starcraft map we generated 150 random solvable problems. For each BG and WC3 map we generated 50 random solvable problems. The paper describes generating random solvable problems on maps for evaluation but does not specify train/validation/test dataset splits.
Hardware Specification Yes All experiments were ran on a 2.60GHz Intel Core i7 under Linux.
Software Dependencies No All algorithms have a common code base and use a standard binary heap for Open. The paper does not provide specific version numbers for any software dependencies.
Experiment Setup No The paper describes the algorithms used and the different values of 'k' for the neighborhoods but does not provide specific experimental setup details such as hyperparameters, optimization settings, or training schedules.