Monte-Carlo Simulation Adjusting

Authors: Nobuo Araki, Masakazu Muramatsu, Hoki Kunihito, Satoshi Takahashi

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In the first experiment, we used a set of 4 4 Go problems, Kuroneko-no Yonro (Hsu 2012), to measure the performance. These problems are relatively difficult, but the correct answers are available.
Researcher Affiliation Academia Nobuo Araki*, Masakazu Muramatsu, Kunihito Hoki, Satoshi Takahashi Graduate School of Informatics and Engineering, The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585 Japan *a1341001@edu.cc.uec.ac.jp
Pseudocode Yes Algorithm 1 Adjusting θ
Open Source Code No The paper does not include an unambiguous statement that the authors are releasing the source code for the work described in this paper, nor does it provide a direct link to a code repository for the methodology.
Open Datasets Yes We test Algorithm 1 on Kuroneko-no Yonro (Hsu 2012), a set of problems of 4 4 Go.
Dataset Splits No The paper states 'We divide them into training data A (60 problems) and test data B (10 problems)', but does not explicitly mention a validation split or its details.
Hardware Specification No The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments.
Software Dependencies No The paper does not provide specific software dependencies, such as programming languages, libraries, or frameworks with their version numbers.
Experiment Setup Yes For each legal move, we carry out 50 simulations to compute its winning rate (M= 50) and 50 simulations to compute ψ (N= 50). We set the loop limit to 150. In our experiment, we set α = 1.0 10 log(L 2.0+3.0) where L is the iteration number so that the step size decreases in accordance with the progress of the iterations.