Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A model for reasoning about persistence and causation
Computational Intelligence
Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Speech recognition with dynamic Bayesian networks
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Monte Carlo localization: efficient position estimation for mobile robots
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
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Approximate learning of dynamic models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Stochastic dynamic programming with factored representations
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Using Learning for Approximation in Stochastic Processes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The EQ Framework for Learning Equivalence Classes of Bayesian Networks
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Relational sequential inference with reliable observations
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
The Journal of Machine Learning Research
Multi-task reinforcement learning: a hierarchical Bayesian approach
Proceedings of the 24th international conference on Machine learning
Learning how to combine sensory-motor functions into a robust behavior
Artificial Intelligence
Apprenticeship learning for helicopter control
Communications of the ACM - Barbara Liskov: ACM's A.M. Turing Award Winner
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Perseus: randomized point-based value iteration for POMDPs
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
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
R-MAX: a general polynomial time algorithm for near-optimal reinforcement learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Optimized execution of action chains using learned performance models of abstract actions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Robot introspection through learned hidden Markov models
Artificial Intelligence
Automatic programming of behavior-based robots using reinforcement learning
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
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
The Information bottleneck EM algorithm
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Comparison of score metrics for Bayesian network learning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Complex artifacts are designed today from well specified and well modeled components. But most often, the models of these components cannot be composed into a global functional model of the artifact. A significant observation, modeling and identification effort is required to get such a global model, which is needed in order to better understand, control and improve the designed artifact.Robotics provides a good illustration of this need. Autonomous robots are able to achieve more and more complex tasks, relying on more advanced sensor-motor functions. To better understand their behavior and improve their performance, it becomes necessary but more difficult to characterize and to model, at the global level, how robots behave in a given environment. Low-level models of sensors, actuators and controllers cannot be easily combined into a behavior model. Sometimes high level models operators used for planning are also available, but generally they are too coarse to represent the actual robot behavior.We propose here a general framework for learning from observation data the behavior model of a robot when performing a given task. The behavior is modeled as a Dynamic Bayesian Network, a convenient stochastic structured representations. We show how such a probabilistic model can be learned and how it can be used to improve, on line, the robot behavior with respect to a specific environment and user preferences. Framework and algorithms are detailed; they are substantiated by experimental results for autonomous navigation tasks.