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REST: a reliable estimation of stopping time algorithm for social game experiments

Published: 14 April 2015 Publication History

Abstract

Through a social game, we integrate building occupants into the control and management of an office building that is instrumented with networked embedded systems for sensing and actuation. The goal of the social game is to both incentivize building occupants to be more energy efficient and learn behavioral models for occupants so that the building can be made sustainable through automation. Given a generative model for the occupants behavior in the competitive environment created by the social game, we develop a method for learning the parameters of the behavioral model as we conduct the experiment by adopting a learning to learn framework. Using tools from statistical learning, we provide bounds on the parameter inference error. In addition, we provide an algorithm for computing the stopping time required for a specified level of confidence in estimation. We show the performance of our algorithm in several examples.

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  • (2018)A Robust Utility Learning Framework via Inverse OptimizationIEEE Transactions on Control Systems Technology10.1109/TCST.2017.269916326:3(954-970)Online publication date: May-2018

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        cover image ACM Conferences
        ICCPS '15: Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems
        April 2015
        269 pages
        ISBN:9781450334556
        DOI:10.1145/2735960
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 14 April 2015

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        • (2018)A Robust Utility Learning Framework via Inverse OptimizationIEEE Transactions on Control Systems Technology10.1109/TCST.2017.269916326:3(954-970)Online publication date: May-2018

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