.png){kind=icon}
返回
A Robust Utility Learning Framework via Inverse Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TCST.2017.2699163.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In many smart infrastructure applications, flexibility in achieving sustainability goals can be gained by engaging end users. However, these users often have heterogeneous preferences that are unknown to the decision maker tasked with improving operational efficiency. Modeling user interaction as a continuous game between noncooperative players, we propose a robust parametric utility learning framework that employs constrained feasible generalized least squares estimation with heteroskedastic inference. To improve forecasting performance, we extend the robust utility learning scheme by employing bootstrapping with bagging, bumping, and gradient boosting ensemble methods. Moreover, we estimate the noise covariance, which provides approximated correlations between players, which we leverage to develop a novel correlated utility learning framework. We apply the proposed methods both to a toy example arising from Bertrand-Nash competition between two firms and to data from a social game experiment designed to encourage energy efficient behavior among smart building occupants. Using occupant voting data for shared resources such as lighting, we simulate the game defined by the estimated utility functions to demonstrate the performance of the proposed methods.
Keyword:
Game theory
inverse optimization
smart building energy efficiency
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
4.9K
被引数:
1.7W
机构
引用论文
Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Bottom-up approach for the reaction of xenobiotics and their metabolites with model substances for natural organic matter by electrochemistry–mass spectrometry (EC–MS)Chemosphere
ChemosphereIF0
PREAI
分享收藏Antiferromagnetic interlayer coupling of (111)-oriented La
0.67
Sr
0.33
MnO
3
/SrRuO
3
superlattices
PREAI
分享收藏
查看更多内容