Bounded-parameter Markov decision process
Artificial Intelligence
An agent-based simulation-assisted approach to bi-lateral building systems control
An agent-based simulation-assisted approach to bi-lateral building systems control
Improving adjustable autonomy strategies for time-critical domains
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
Agent-based control for decentralised demand side management in the smart grid
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Deploying power grid-integrated electric vehicles as a multi-agent system
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
ESCAPES: evacuation simulation with children, authorities, parents, emotions, and social comparison
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Markov decision processes with multiple objectives
STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
Sustainable multiagent application to conserve energy (demonstration)
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
TESLA: an energy-saving agent that leverages schedule flexibility
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Demand-driven power saving by multiagent negotiation for HVAC control
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
TESLA: an extended study of an energy-saving agent that leverages schedule flexibility
Autonomous Agents and Multi-Agent Systems
A survey of multi-objective sequential decision-making
Journal of Artificial Intelligence Research
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This paper describes an innovative multiagent system called SAVES with the goal of conserving energy in commercial buildings. We specifically focus on an application to be deployed in an existing university building that provides several key novelties: (i) jointly performed with the university facility management team, SAVES is based on actual occupant preferences and schedules, actual energy consumption and loss data, real sensors and hand-held devices, etc.; (ii) it addresses novel scenarios that require negotiations with groups of building occupants to conserve energy; (iii) it focuses on a non-residential building, where human occupants do not have a direct financial incentive in saving energy and thus requires a different mechanism to effectively motivate occupants; and (iv) SAVES uses a novel algorithm for generating optimal MDP policies that explicitly consider multiple criteria optimization (energy and personal comfort) as well as uncertainty over occupant preferences when negotiating energy reduction -- this combination of challenges has not been considered in previous MDP algorithms. In a validated simulation testbed, we show that SAVES substantially reduces the overall energy consumption compared to the existing control method while achieving comparable average satisfaction levels for occupants. As a real-world test, we provide results of a trial study where SAVES is shown to lead occupants to conserve energy in real buildings.