@conference {1504603,
	title = {Towards Robust Multi-objective Optimization Under Model Uncertainty for Energy Conservation },
	booktitle = {Workshop on Agent Technologies for Energy Systems (ATES) at AAMAS	},
	year = {2012},
	abstract = {to the significant growth in energy usage. Building multiagent systems for real-world energy applications raises several research challenges regarding scalability, optimizing multiple competing objectives, model uncertainty, and complexity in deploying the system.
Motivated by these challenges, this paper proposes a new approach
to effectively conserve building energy. This work contributes to a
very new area that requires considering large-scale multi-objective
optimization as well as uncertainty over occupant preferences when
negotiating energy reduction. There are three major contributions.
We (i) develop a new method called HRMM to compute robust
solutions in practical situations; (ii) experimentally show that obtained strategies from HRMM converge to near-optimal solutions;
and (iii) provide a systematic way to tightly incorporate the insights
from human subject studies into our computational model and algorithms. The HRMM method is verified in a validated simulation
testbed in terms of energy savings and comfort levels of occupants},
	author = {Kwak, Jun-young and Varakantham, Pradeep and Maheswaran, Rajiv and Tambe, Milind and Timothy Hayes and Wood, Wendy and Becerik-Gerber, Burcin}
}
