Modeling Precomputation In Games Played Under Computational Constraints
Authors: Thomas Orton
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments measuring the trade-off between randomness and precomputation are provided for Stockfish (a wellknown chess playing algorithm).", "We therefore choose to focus on this aspect, and propose an experiment to numerically explore the popular chess engine Stockfish s susceptibility to precomputation using Algorithm 1. |
| Researcher Affiliation | Academia | Thomas Orton University of Oxford thomas.orton@cs.ox.ac.uk |
| Pseudocode | Yes | The following Theorem makes these ideas precise, and the detailed algorithm (Algorithm 1) can be found in the technical appendix.", "The following theorem makes these details precise, and the algorithm (Algorithm 2) can be found in the technical appendix. |
| Open Source Code | Yes | Further experimentation details can be found in the appendix, and the full code and technical appendix can be found on Github.6 https://github.com/Thomas-Orton/chess-precomputation. |
| Open Datasets | No | The paper uses the Stockfish chess engine to generate game states for experiments, but it does not specify a publicly available or open dataset in the traditional sense (e.g., ImageNet, CIFAR-10) with a specific link, DOI, or repository for access. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions using 'Stockfish' as a chess engine but does not provide specific version numbers for Stockfish or any other software dependencies, such as libraries or programming languages. |
| Experiment Setup | Yes | We set λ1 = λ2 = 10 5 in these experiments, and plot the precomputed strategy utility (without precomputation penalty) and memorization set size for varying levels of randomness. In order to keep the computation requirements modest, only the top K = 2 moves of the Stockfish engine were considered at each board position. Specifically, we choose σpre to be Stockfish with 50ms per move, while σ1 and σ2 play as Stockfish with 10ms per move. |