Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimal Machine Strategies to Commit to in Two-Person Repeated Games
Authors: Song Zuo, Pingzhong Tang
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We begin with the simple case where both players are confined to automata strategies, and then extend to general (possibly randomized) machine strategies. We first give a concise linear program to compute the optimal leader s strategy and give two efficient implementations of the linear program: one via enumeration of a convex hull and the other via randomization. We prove by construction that the optimal automata strategy to commit to exists and can be found in linear time. |
| Researcher Affiliation | Academia | Song Zuo and Pingzhong Tang Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Solving for case 2. and Algorithm 2: Suppressing the security level of player 2. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing open-source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a public dataset in the traditional sense (e.g., for model training or evaluation). It uses game theory examples like the Prisoner's Dilemma, which is a conceptual game, not a dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation on data, therefore, no training/validation/test dataset splits are provided. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for computation or experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and algorithm design; therefore, it does not include details on experimental setup such as hyperparameters or system-level training settings. |