Learning Optimal Strategies to Commit To
Authors: Binghui Peng, Weiran Shen, Pingzhong Tang, Song Zuo2149-2156
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the problem of learning the optimal leader strategy in Stackelberg (security) games and develop novel algorithms as well as new hardness results. We aim to tackle this problem in this paper and we develop novel algorithms as well as hardness results regarding the sample complexity. For general Stackelberg games, we propose an algorithm, named SEPARATE-SEARCH, whose sample complexity is poly(m, n, L)Ext(A). We derive hardness results which show that the exponential dependence of m is inevitable. |
| Researcher Affiliation | Collaboration | Binghui Peng,1 Weiran Shen,1 Pingzhong Tang,1 Song Zuo2 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Google Research pbh15@mails.tsinghua.edu.cn, emersonswr, kenshinping@gmail.com, szuo@google.com |
| Pseudocode | Yes | Algorithm 1 SEPARATE-SEARCH, Algorithm 2 EXHAUSTIVE-SEARCH, Algorithm 3 SECURITY-SEARCH |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper focuses on theoretical algorithms and complexity analysis, not on empirical studies involving data training. No dataset access information is provided. |
| Dataset Splits | No | The paper focuses on theoretical algorithms and complexity analysis and does not describe experiments that would involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical work and does not describe experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper presents theoretical algorithms and does not specify any software dependencies with version numbers required for implementation or reproduction. |
| Experiment Setup | No | The paper focuses on theoretical algorithms and complexity analysis, not on empirical experiments requiring specific setup details like hyperparameters or training configurations. |