Artificial Decision Making Under Uncertainty in Intelligent Buildings
  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.
