Lifting Model Sampling for General Game Playing to Incomplete-Information Models
Authors: Michael Schofield, Michael Thielscher
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the success of this technique over standard model sampling. A series of experiments was designed to test the capabilities of the new technique using the high-throughput computer facilities at the School of Computer Science and Engineering. |
| Researcher Affiliation | Academia | Michael Schofield and Michael Thielscher School of Computer Science and Engineering The University of New South Wales {mschofield, mit}@cse.unsw.edu.au. The second author is also affiliated with the University of Western Sydney. |
| Pseudocode | No | The paper provides formal definitions and mathematical expressions for the techniques but does not include structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper refers to custom game scenarios (Exploding Bomb, Spy vs. Spy, Number Guessing, Banker and Thief, Battleships In Fog) used in the experiments. While the GDL for 'Exploding Bomb' is shown, there is no explicit mention or link to a publicly available dataset in the conventional sense that would allow external access for training or testing. |
| Dataset Splits | No | The paper discusses experimental results for game playing but does not provide specific details on training, validation, or test dataset splits, as it's not based on traditional machine learning datasets. |
| Hardware Specification | No | The paper mentions using 'high-throughput computer facilities at the School of Computer Science and Engineering' but does not provide any specific details about the hardware, such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions Game Description Language (GDL) and GDL-II, but does not provide specific version numbers for any software, programming languages, or libraries used in the implementation or experiments. |
| Experiment Setup | Yes | In each experiment the player resources were varied to demonstrate the performance as a function of resources. ... a value of n = 4 in eval(s, π, r, n) for the original technique... The new technique would visit 2,048,000 states. However experiments showed that the new technique can function optimally... That is, where the original player might need n = 16, the new player only requires n = 4. |