K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
Computationally feasible bounds for partially observed Markov decision processes
Operations Research
Technical Note: \cal Q-Learning
Machine Learning
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
The design, implementation and evaluation of SMART: a scheduler for multimedia applications
Proceedings of the sixteenth ACM symposium on Operating systems principles
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
The sensor selection problem for bounded uncertainty sensing models
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
ACM Computing Surveys (CSUR)
Letting loose a SPIDER on a network of POMDPs: generating quality guaranteed policies
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Forward search value iteration for POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
People tracking with anonymous and ID-sensors using Rao-Blackwellised particle filters
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A heuristic variable grid solution method for POMDPs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
PEGASUS: a policy search method for large MDPs and POMDPs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
The factored frontier algorithm for approximate inference in DBNs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Prioritizing point-based POMDP solvers
ECML'06 Proceedings of the 17th European conference on Machine Learning
An adaptive strategy for energy-efficient data collection in sparse wireless sensor networks
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
Approximate Dynamic Programming for Communication-Constrained Sensor Network Management
IEEE Transactions on Signal Processing
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
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We present a framework for sensor actuation and control in sentient spaces, in which sensors are used to observe a physical phenomena. We focus on sentient spaces that enable pervasive computing applications, such as smart video surveillance and situational awareness in instrumented office environments. Our framework utilizes the spatio-temporal statistical properties of an observed phenomena, with the goal of maximizing an application-specified reward. Specifically, we define an observation of a phenomena by assigning it a discrete value (state) and we model its semantics as the transition between these values (states). This semantic model is used to predict the future states in which the phenomena is likely to be at, based on partially-observed past states. To accomplish real-time agility, we designed an approximate, adaptive-grid solution for Partially Observable Markov Decision Processes (POMDPs) that yields practically good results, and in some cases, guarantees on the quality of the approximation. We use our framework to control and actuate a large-scale camera network so as to maximize the number and type of captured events. To enable real-time control, we implement an action schedule using a table lookup and make use of a factored probability model to capture state semantics. To the best of our knowledge, we are the first to address the problem of actuating a large-scale sensor network based on a real-time POMDP formulation.