Generalized Target Assignment and Path Finding Using Answer Set Programming
Authors: Van Nguyen, Philipp Obermeier, Tran Cao Son, Torsten Schaub, William Yeoh
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experimental evaluations on three benchmarks with different G-TAPF variants, including the TAPF formulation. |
| Researcher Affiliation | Academia | Van Nguyen Computer Science Department New Mexico State University Philipp Obermeier Computer Science Department University of Potsdam Tran Cao Son Computer Science Department New Mexico State University Torsten Schaub Computer Science Department University of Potsdam William Yeoh Computer Science Department New Mexico State University |
| Pseudocode | Yes | Algorithm 1: inc GTAPF(P) Algorithm 2: d GTAPF(P) |
| Open Source Code | Yes | The source code and a demo of this experiment is available at https://potassco.org/labs/. |
| Open Datasets | No | The paper uses "three benchmarks" called Gridworld, CORRIDOR, and Autonomous Warehouse System. For CORRIDOR, it describes how the benchmark is parameterized, and for the warehouse system, it mentions "various warehouse configurations similar to the one in Figure 1", but does not provide specific access information (link, DOI, formal citation) to publicly available datasets used for training. |
| Dataset Splits | No | The paper discusses various problem instances for its experimental evaluation, but it does not provide specific details on dataset splits (e.g., train/validation/test percentages or counts) for reproducibility. |
| Hardware Specification | Yes | We conducted our experiments on a 3.60GHz CPU machine with 8GB of RAM, and set a timeout of 1800s. |
| Software Dependencies | No | The paper mentions using 'ASP solvers' and 'clingo [Gebser et al., 2014]' along with 'Python procedures', but it does not specify concrete version numbers for these software components (e.g., Python 3.x, Clingo 5.x), which are necessary for reproducible dependency information. |
| Experiment Setup | No | The paper mentions setting a 'timeout of 1800s' for experiments, but it does not provide specific experimental setup details such as hyperparameters, learning rates, batch sizes, or other system-level training settings common in detailed reproducibility sections. |