  Decision making in large scale urban environments is critical for many
applications involving continuous distribution of resources and utilization of
infrastructure, such as ambient lighting control and traffic management.
Traditional decision making methods involve extensive human participation, are
expensive, and inefficient and unreliable for hard-to-predict situations.
Modern technology, including ubiquitous data collection though sensors,
automated analysis and prognosis, and online optimization, offers new
capabilities for developing flexible, autonomous, scalable, efficient, and
predictable control methods. This paper presents a new decision making concept
in which a hierarchy of semantically more abstract models are utilized to
perform online scalable and predictable control. The lower semantic levels
perform localized decisions based on sampled data from the environment, while
the higher semantic levels provide more global, time invariant results based on
aggregated data from the lower levels. There is a continuous feedback between
the levels of the semantic hierarchy, in which the upper levels set performance
guaranteeing constraints for the lower levels, while the lower levels indicate
whether these constraints are feasible or not. Even though the semantic
hierarchy is not tied to a particular set of description models, the paper
illustrates a hierarchy used for traffic management applications and composed
of Finite State Machines, Conditional Task Graphs, Markov Decision Processes,
and functional graphs. The paper also summarizes some of the main research
problems that must be addressed as part of the proposed concept
