Agent Behavior Prediction and Its Generalization Analysis
Authors: Fei Tian, Haifang Li, Wei Chen, Tao Qin, Enhong Chen, Tie-Yan Liu
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the convergence of the new Markov chain when time approaches infinity. Then we obtain a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain. To the best of our knowledge, this is the first work that performs the generalization analysis on data generated by complex processes in real-world dynamic systems. In this paper, we propose to use Markov Chain in Random Environments (MCRE) to describe the behavior data, and perform generalization analysis of machine learning algorithms on its basis. |
| Researcher Affiliation | Collaboration | University of Science and Technology of China tianfei@mail.ustc.edu.cn Chinese Academy of Sciences lihaifang@amss.ac.cn Wei Chen Microsoft Research wche@microsoft.com Tao Qin Microsoft Research taoqin@microsoft.com Enhong Chen University of Science and Technology of China cheneh@ustc.edu.cn Tie-Yan Liu Microsoft Research tyliu@microsoft.com This work was done when the first two authors were visiting Microsoft Research Asia. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or statements about its availability. As a theoretical paper focused on proofs and analysis, it does not involve an implementable methodology requiring code release. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. No information about datasets or their availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets. No training/test/validation splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and analysis. It does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software components with version numbers needed for replication. |
| Experiment Setup | No | The paper is theoretical and does not include an experimental setup with hyperparameters or system-level training settings. |