Computationally feasible bounds for partially observed Markov decision processes
Operations Research
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
GTM: the generative topographic mapping
Neural Computation
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Variable Resolution Discretization in Optimal Control
Machine Learning
Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
BI-POMDP: Bounded, Incremental, Partially-Observable Markov-Model Planning
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Speeding up the convergence of value iteration in partially observable Markov decision processes
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An improved grid-based approximation algorithm for POMDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Policy-contingent abstraction for robust robot control
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Integrating Value-Directed Compression and Belief Space Analysis for POMDP Decomposition
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
A novel orthogonal NMF-based belief compression for POMDPs
Proceedings of the 24th international conference on Machine learning
Efficient Multi-robot Search for a Moving Target
International Journal of Robotics Research
Training a real-world POMDP-based dialogue system
NAACL-HLT-Dialog '07 Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies
Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems
Compact, convex upper bound iteration for approximate POMDP planning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A frame-based probabilistic framework for spoken dialog management using dialog examples
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Monte Carlo sampling methods for approximating interactive POMDPs
Journal of Artificial Intelligence Research
Nonmyopic adaptive informative path planning for multiple robots
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
On Compressibility and Acceleration of Orthogonal NMF for POMDP Compression
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Probabilistic action planning for active scene modeling in continuous high-dimensional domains
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Improving POMDP tractability via belief compression and clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual search for an object in a 3D environment using a mobile robot
Computer Vision and Image Understanding
Planning under Uncertainty for Robotic Tasks with Mixed Observability
International Journal of Robotics Research
Representing uncertainty about complex user goals in statistical dialogue systems
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Motion planning under uncertainty for robotic tasks with long time horizons
International Journal of Robotics Research
Identifying and exploiting weak-information inducing actions in solving POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Search and pursuit-evasion in mobile robotics
Autonomous Robots
Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance
Journal of Intelligent and Robotic Systems
Proceedings of the 5th ACM workshop on Security and artificial intelligence
Decentralized multi-robot cooperation with auctioned POMDPs
International Journal of Robotics Research
Planning for multiple measurement channels in a continuous-state POMDP
Annals of Mathematics and Artificial Intelligence
Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy
Pervasive and Mobile Computing
Poisson Noise Reduction with Non-local PCA
Journal of Mathematical Imaging and Vision
Point-based online value iteration algorithm in large POMDP
Applied Intelligence
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Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the entire belief space. However, in real-world POMDP problems, computing the optimal policy for the full belief space is often unnecessary for good control even for problems with complicated policy classes. The beliefs experienced by the controller often lie near a structured, low-dimensional subspace embedded in the high-dimensional belief space. Finding a good approximation to the optimal value function for only this subspace can be much easier than computing the full value function. We introduce a new method for solving large-scale POMDPs by reducing the dimensionality of the belief space. We use Exponential family Principal Components Analysis (Collins, Dasgupta, & Schapire, 2002) to represent sparse, high-dimensional belief spaces using small sets of learned features of the belief state. We then plan only in terms of the low-dimensional belief features. By planning in this low-dimensional space, we can find policies for POMDP models that are orders of magnitude larger than models that can be handled by conventional techniques. We demonstrate the use of this algorithm on a synthetic problem and on mobile robot navigation tasks.