General Statistical Approaches to Procedural Map Generation
Authors: Sam Snodgrass
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We are using Super Mario Bros., Loderunner, and Kid Icarus as the initial testing domains for the above algorithms. ... We use several metrics to evaluate the generated maps and the chosen algorithm. |
| Researcher Affiliation | Academia | Sam Snodgrass Drexel University, Department of Computer Science Philadelphia, PA, USA Sps74@Drexel.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code or explicitly state that the code is open-source. |
| Open Datasets | No | The paper mentions using 'training maps' and 'training data' and lists 'Super Mario Bros., Loderunner, and Kid Icarus' as testing domains, implying they are also used for training. However, it does not provide concrete access information (e.g., links, DOIs, specific citations with authors and year for a publicly available dataset) for these training maps. |
| Dataset Splits | No | The paper mentions 'training maps' and 'testing domains' but does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper discusses the use of Markov models (Md MCs and MRFs) but does not provide specific ancillary software details with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper describes the learning algorithms and evaluation metrics but does not contain specific experimental setup details such as hyperparameter values, optimizer settings, or other concrete training configurations. |