Using Conditional Random Fields for Decision-Theoretic Planning

Authors:
Paul A. Ardis;Christopher M. Brown
Affiliations:
University of Rochester, Rochester 14627;University of Rochester, Rochester 14627
Venue:
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Year:
2009

Citing 9
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Solving very large weakly coupled Markov decision processes

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Markov logic networks

Machine Learning
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

The Journal of Machine Learning Research
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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
Unconstrained influence diagrams

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a means of extending Conditional Random Field modeling to decision-theoretic planning where valuation is dependent upon fully-observable factors. Representation is discussed, and a comparison with existing decision problem methodologies is presented. Included are exact and inexact message passing schemes for policy making, examples of decision making in practice, extensions to solving general decision problems, and suggestions for future use.