Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems
Information driven search for point sources of gamma radiation
Signal Processing
Coordinated guidance of autonomous UAVs via nominal belief-state optimization
ACC'09 Proceedings of the 2009 conference on American Control Conference
Switching between different state representations in reinforcement learning
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Information theoretic adaptive tracking of epidemics in complex networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Sensor management: a new paradigm for automatic video surveillance
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained. The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times. Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.