Nested Monte Carlo Search for Two-Player Games
Authors: Tristan Cazenave, Abdallah Saffidine, Michael Schofield, Michael Thielscher
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental data, obtained on a variety of two-player games from past General Game Playing (GGP) competitions and others, demonstrate the usefulness of these techniques in a Nested Player when pitted against a standard, optimised UCT player. |
| Researcher Affiliation | Academia | Tristan Cazenave LAMSADE Universit e Paris-Dauphine cazenave@lamsade.dauphine.fr Abdallah Saffidine Michael Schofield Michael Thielscher School of Computer Science and Engineering The University of New South Wales {abdallahs, mschofield, mit}@cse.unsw.edu.au |
| Pseudocode | Yes | Algorithm 1: Two-player two-outcome NMCS. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper conducts experiments on 9 two-player games (e.g., Breakthrough, Domineering) from GGP competitions. It simulates games (e.g., "500 games are played per match") rather than using pre-collected, publicly available datasets with specific access information (links, DOIs, or formal citations). |
| Dataset Splits | No | The paper describes running game simulations and matches (e.g., "500 games are played per match"), but it does not specify explicit training, validation, or test dataset splits in terms of percentages, sample counts, or predefined partition files, as is common with static datasets. |
| Hardware Specification | No | The experiments are run on a 3.0 GHz PC under Linux. This provides a clock speed and operating system but does not specify a particular CPU model (e.g., Intel Core i7-xxxx) or GPU model, which would be considered specific hardware details. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | We try several parameterisation of NMCS: nesting depth 1 or 2, COW, and the combination of discounting and POD. For each game, each parameter setting, and each time constraint, we run a 500 games match where NMCS plays as first player 250 times and we record how frequently NMCS wins in Table 4. |