ABSTRACT
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135
Most traditional physical devices and systems have a single authority that is responsible for system-level decision making. This authority-whether a physical device (e.g., a control circuit), a cybertechnology (an algorithm or piece of software), or a human-holds primary responsibility for overall strategic control and management of system dynamics. The embedding of cybertechnologies into the physical world, and the consequent networking of things, is changing the traditional
paradigm: modern cyber-physical systems (CPS) and networks often contain multiple heterogeneous intelligences, which must coordinate for decision making [1-3]. These intelligent agents involved in decisionmaking are often quite diverse, involving heterogeneous physical-world systems, cybertechnologies, and humans. Decision making for the Internet of Things thus crucially requires new frameworks, and associated algorithms and models, for coordination of diverse physical-world, cyber, and human agents.
