Optimal Machine Strategies to Commit to in Two-Person Repeated Games
Authors: Song Zuo, Pingzhong Tang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. |