Computer-Aided Game Design: Doctoral Consortium Research Abstract
Authors: Aaron Isaksen
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our team automated this process by creating AI agents that play Flappy Bird [Nguyen, 2013]2, making the same types of errors that humans make in simple minimal one-button games. ... By automating the play test process, this allowed us to repeat the process tens of thousands of times over a range of input parameters, using a Monte Carlo simulation to generate a histogram of expected scores. To analyze these histograms, I used survival analysis... |
| Researcher Affiliation | Academia | Aaron Isaksen Tandon School of Engineering, New York University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is available, nor does it provide a link to a repository. |
| Open Datasets | No | The paper mentions using Flappy Bird for simulations but does not provide concrete access information (link, DOI, repository, or formal citation to a dataset) for any publicly available or open dataset used for training or analysis. |
| Dataset Splits | No | The paper does not specify exact percentages or sample counts for training, validation, or test dataset splits, nor does it reference predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the general approach of the simulations and analysis (e.g., AI agent modeling, Monte Carlo simulation, genetic optimizer) but does not provide specific numerical hyperparameter values or detailed system-level training settings. |