TDS+: Improving Temperature Discovery Search
Authors: Yeqin Zhang, Martin Müller
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All experiments use the game of Amazons and are performed on a 2.4 GHz Intel Xeon. |
| Researcher Affiliation | Academia | Yeqin Zhang and Martin M uller Computing Science, University of Alberta Edmonton, Canada |
| Pseudocode | Yes | Algorithm 1 Conditional Move Generation using a temperature-dependent Skip() test. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the release of source code for the methodology described. |
| Open Datasets | No | The paper mentions using 'the game of Amazons' and 'a complete database of 4 4 Amazons positions' but does not provide concrete access information (link, DOI, or formal citation for accessing the dataset). |
| Dataset Splits | No | The paper describes the sampling of test cases ('600 cases from a complete database... These were randomly sampled') and refers to 'test set' in tables, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or specific split methodology) for reproduction. |
| Hardware Specification | Yes | All experiments use the game of Amazons and are performed on a 2.4 GHz Intel Xeon. The maximum memory in the experiment is 80 MB. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with specific versions) used to replicate the experiments. |
| Experiment Setup | Yes | The experimental setting is the same as the setting in Table 3 of (M uller, Enzenberger, and Schaeffer 2004). Each subgame contains one Amazon of each player, plus some random obstacles. As in the experiment mentioned above, results are averaged over 200 runs with different randomly generated subgames where one queen each and three burnt-off squares were placed into each subgame at random locations. In the first two experiments, the time limit is 10 seconds per move. |