Qualitative Reasoning About Cardinal Directions Using Answer Set Programming
Authors: Yusuf Izmirlioglu, Esra Erdem
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We discuss the useful aspects of our ASP-based method for reasoning over cardinal directions with some examples, and provide experimental evaluations. We have evaluated both formulations over 12 scenarios where directional relations are described by basic CDC relations with incomplete networks specified over n = 4, 6, 8 connected/disconnected spatial objects. |
| Researcher Affiliation | Academia | Yusuf Izmirlioglu, Esra Erdem Sabanci University Istanbul, Turkey |
| Pseudocode | No | The paper includes logical rules and mathematical definitions, but not structured pseudocode or algorithm blocks with specific labels. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | No | The paper discusses how spatial objects are represented as grid cells ('Λm'), but it does not mention the use of a publicly available dataset or provide access information for any specific dataset used in experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or methodology for data partitioning). |
| Hardware Specification | Yes | The experiments are performed on a Linux server with Intel E5-2665 CPU with 2.4GHz and 64GB memory, using the ASP solver CLINGO 4.5.4. |
| Software Dependencies | Yes | The experiments are performed on a Linux server with Intel E5-2665 CPU with 2.4GHz and 64GB memory, using the ASP solver CLINGO 4.5.4. |
| Experiment Setup | No | The paper describes the evaluation over different scenarios (e.g., 'n = 4, 6, 8 connected/disconnected spatial objects', 'Consistent/Inconsistent'), but does not provide specific hyperparameter values or system-level training settings typically found in experimental setups. |