7,608 research outputs found
A normative approach to multi-agent systems for intelligent buildings
Building Management Systems (BMS) are widely adopted in modern buildings around the world in order to
provide high-quality building services, and reduce the running cost of the building. However, most BMS are
functionality-oriented and do not consider user personalization. The aim of this research is to capture and
represent building management rules using organizational semiotics methods. We implement Semantic
Analysis, which determines semantic units in building management and their relationship patterns of
behaviour, and Norm Analysis, which extracts and specifies the norms that establish how and when these
management actions occur. Finally, we propose a multi-agent framework for norm based building
management. This framework contributes to the design domain of intelligent building management system
by defining a set of behaviour patterns, and the norms that govern the real-time behaviour in a building
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Recent advances in combining deep neural network architectures with
reinforcement learning techniques have shown promising potential results in
solving complex control problems with high dimensional state and action spaces.
Inspired by these successes, in this paper, we build two kinds of reinforcement
learning algorithms: deep policy-gradient and value-function based agents which
can predict the best possible traffic signal for a traffic intersection. At
each time step, these adaptive traffic light control agents receive a snapshot
of the current state of a graphical traffic simulator and produce control
signals. The policy-gradient based agent maps its observation directly to the
control signal, however the value-function based agent first estimates values
for all legal control signals. The agent then selects the optimal control
action with the highest value. Our methods show promising results in a traffic
network simulated in the SUMO traffic simulator, without suffering from
instability issues during the training process
NASA space station automation: AI-based technology review. Executive summary
Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics
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