Artificial Decision Making Under Uncertainty in Intelligent Buildings
This addresses energy efficiency and user experience in smart buildings, but appears incremental as it relaxes assumptions in existing agent control methods.
The paper tackles the problem of improving energy saving and customer value in intelligent buildings by equipping multi-agent systems with automated decision support, showing that room-controlling agents can make bounded rational decisions under dynamic real-time constraints.
Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be increased. The first is energy saving in the building. The second is customer value---how the people in the building experience the effects of the actions of the agents. We give evidence for the truth of this hypothesis through experimental findings related to tools for artificial decision making. A number of assumptions related to agent control, through monitoring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one uncertainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic decision situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.