Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Compromise-free Pathfinding on a Navigation Mesh
Authors: Michael Cui, Daniel D. Harabor, Alban Grastien
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show how that algorithm can be modified to support search over arbitrary sets of convex polygons and then evaluate its performance on a range of realistic and synthetic benchmark problems. 6 Empirical Analysis We test Polyanya on a variety of realistic and synthetic grid benchmarks which are described in [Sturtevant, 2012]. |
| Researcher Affiliation | Academia | Michael L. Cui Monash University Melbourne, Australia EMAIL Daniel D. Harabor Monash University Melbourne, Australia EMAIL Alban Grastien Data61, Canberra Australian National University EMAIL |
| Pseudocode | No | The paper describes the algorithm components but does not provide structured pseudocode or an explicit algorithm block. |
| Open Source Code | Yes | All of our source code is publicly available4. 4https://bitbucket.org/dharabor/pathfinding |
| Open Datasets | Yes | All benchmarks are available from the HOG2 online repository 2. 2https://github.com/nathansttt/hog2 |
| Dataset Splits | No | The paper mentions using 'realistic and synthetic grid benchmarks' but does not specify any dataset splits (e.g., train/validation/test percentages or counts) or cross-validation setup. |
| Hardware Specification | Yes | All experiments are performed on a 1.7 GHz Intel Core i5 machine with 4GB of RAM and running Linux 4.8.13. |
| Software Dependencies | Yes | We implemented Polyanya in C++ and compiled our code with g++ 6.3.1 using -O3. |
| Experiment Setup | No | The paper describes the algorithm's design and empirical evaluation but does not provide specific experimental setup details such as hyperparameters, training configurations, or other system-level settings beyond the general hardware and compiler used. |