Two procedures for compiling influence diagrams

Authors:
Paul E. Lehner;Azar Sadigh
Affiliations:
Systems Engineering Dept., George Mason University, Fairfax, VA;Systems Engineering Dept., George Mason University, Fairfax, VA
Venue:
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Year:
1993

Citing 6
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
On the generation of organizational architectures using Petri nets

Advances in Petri Nets 1988
The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
Integrating probabilistic rules into neural network: a stochastic EM learning algorithms

Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Planning and control

Planning and control
Superior design features of decision analytic software

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Two algorithms are presented for "compiling" influence diagrams into a set of simple decision rules. These decision rules define simple-to-execute, complete, consistent, and near-optimal decision procedures. These compilation algorithms can be used to derive decision procedures for human teams Solving time constrained decision problems.