A model for reasoning about persistence and causation
Computational Intelligence
Elements of information theory
Elements of information theory
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Using Learning for Approximation in Stochastic Processes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A scheme for approximating probabilistic inference
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning agents for uncertain environments (extended abstract)
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Structured representation of complex stochastic systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Exploiting the architecture of dynamic systems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Simulation-based inference for plan monitoring
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Adaptive, Model-Based Monitoring for Cyber Attack Detection
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
Arc Weights for Approximate Evaluation of Dynamic Belief Networks
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Extending a DBMS to Support Content-Based Video Retrieval: A Formula 1 Case Study
EDBT '02 Proceedings of the Worshops XMLDM, MDDE, and YRWS on XML-Based Data Management and Multimedia Engineering-Revised Papers
Tree approximation for belief updating
Eighteenth national conference on Artificial intelligence
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
The Bayes Point Machine for computer-user frustration detection via pressuremouse
Proceedings of the 2001 workshop on Perceptive user interfaces
An online POMDP algorithm for complex multiagent environments
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Intelligent light control using sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Active learning for Hidden Markov Models: objective functions and algorithms
ICML '05 Proceedings of the 22nd international conference on Machine learning
Who's asking for help?: a Bayesian approach to intelligent assistance
Proceedings of the 11th international conference on Intelligent user interfaces
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
U-director: a decision-theoretic narrative planning architecture for storytelling environments
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
The Journal of Machine Learning Research
Convergence in Markovian models with implications for efficiency of inference
International Journal of Approximate Reasoning
Learning and approximate inference in dynamic hierarchical models
Computational Statistics & Data Analysis
Combining object and feature dynamics in probabilistic tracking
Computer Vision and Image Understanding
Approximate predictive state representations
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Exploiting locality of interaction in factored Dec-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Modeling time-varying uncertain situations using Dynamic Influence Nets
International Journal of Approximate Reasoning
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Factored reasoning for monitoring dynamic team and goal formation
Information Fusion
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Probabilistic planning with clear preferences on missing information
Artificial Intelligence
InfoMax Bayesian learning of the Furuta pendulum
Acta Cybernetica
Analytic moment-based Gaussian process filtering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Behaviors Models for Robot Execution Control
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Junction Tree Factored Particle Inference Algorithm for Multi-Agent Dynamic Influence Diagrams
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Functional specification of probabilistic process models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Symbolic heuristic search value iteration for factored POMDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Efficient reinforcement learning in factored MDPs
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A sparse sampling algorithm for near-optimal planning in large Markov decision processes
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Computing factored value functions for policies in structured MDPs
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Peripheral-foveal vision for real-time object recognition and tracking in video
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The value of observation for monitoring dynamic systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic models to assist maintenance of multiple instruments
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Efficient inference in dynamic belief networks with variable temporal resolution
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Mean field variational approximation for continuous-time Bayesian networks
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Knowledge representation for stochastic decision processes
Artificial intelligence today
Fault diagnosis for high-level applications based on dynamic Bayesian network
APNOMS'09 Proceedings of the 12th Asia-Pacific network operations and management conference on Management enabling the future internet for changing business and new computing services
Simultaneous estimation of chords and musical context from audio
IEEE Transactions on Audio, Speech, and Language Processing
Observer for an omnidirectional mobile robot
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Efficient planning in large POMDPs through policy graph based factorized approximations
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Don't fear optimality: sampling for probabilistic-logic sequence models
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Artificial Intelligence
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
Discovering the hidden structure of complex dynamic systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Approximate planning for factored POMDPs using belief state simplification
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Variational learning in mixed-state dynamic graphical models
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Approximate learning in complex dynamic Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Expectation propagation for approximate inference in dynamic bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Factored particles for scalable monitoring
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Value-directed belief state approximation for POMDPs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Probabilistic state-dependent grammars for plan recognition
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Inference in hybrid networks: theoretical limits and practical algorithms
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth 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
Sufficiency, separability and temporal probabilistic models
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Vector-space analysis of belief-state approximation for POMDPs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Value-directed sampling methods for monitoring POMDPs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Monte-Carlo optimizations for resource allocation problems in stochastic network systems
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
ARKAQ-learning: autonomous state space segmentation and policy generation
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
A dynamic Bayesian network based framework to evaluate cascading effects in a power grid
Engineering Applications of Artificial Intelligence
Motion-aided network SLAM with range
International Journal of Robotics Research
Eye movements as time-series random variables: A stochastic model of eye movement control in reading
Cognitive Systems Research
Efficient planning for factored infinite-horizon DEC-POMDPs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Improved use of partial policies for identifying behavioral equivalence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Training factored PCFGs with expectation propagation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Dynamic bayesian networks: a factored model of probabilistic dynamics
ATVA'12 Proceedings of the 10th international conference on Automated Technology for Verification and Analysis
Approximate solutions for factored Dec-POMDPs with many agents
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
Hi-index | 0.00 |
The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system, these tasks typically involve the use of a belief state--a probability distribution over the state of the process at a given point in time. Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. Even in dynamic Bayesian networks (DBNs), where the process itself can be represented compactly, the representation of the belief state is intractable. We investigate the idea of maintaining a compact approximation to the true belief state, and analyze the conditions under which the errors due to the approximations taken over the lifetime of the process do not accumulate to make our answers completely irrelevant. We show that the error in a belief state contracts exponentially as the process evolves. Thus, even with multiple approximations, the error in our process remains bounded indefinitely. We show how the additional structure of a DBN can be used to design our approximation scheme, improving its performance significantly. We demonstrate the applicability of our ideas in the context of a monitoring task, showing that orders of magnitude faster inference can be achieved with only a small degradation in accuracy.