Results 211 to 220 of about 570,997 (263)

Reusable influence diagrams

Artificial Intelligence in Medicine, 1994
Influence Diagrams have been recognized as a suitable formalism for building probabilistic expert systems. Nevertheless, the most part of applications consists in stand-alone systems, concerning a very limited domain. On the other hand, Artificial Intelligence research has outlined Blackboard Architectures as the basis for building expert systems in ...
BELLAZZI, RICCARDO, QUAGLINI, SILVANA
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Decomposition of influence diagrams

Journal of Applied Non-Classical Logics, 2001
When solving a decision problem we want to determine an optimal policy for the decision variables of interest. A policy for a decision variable is in principle a function over its past. However, some of the past may be irrelevant and for both communicational as well as computational reasons it is important not to deal with redundant variables in the ...
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Decomposable Probabilistic Influence Diagrams

Probability in the Engineering and Informational Sciences, 1991
Probabilistic influence diagrams are a useful stochastic modeling tool. To calculate probabilities of interest relative to a probabilistic influence diagram efficiently, it will be helpful for us to use an associated decomposable-directed graph. We first explore and discuss some graph-theoretic and conditional independence properties of decomposable ...
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Gaussian Influence Diagrams

Management Science, 1989
An influence diagram is a network representation of probabilistic inference and decision analysis models. The nodes correspond to variables that can be either constants, uncertain quantities, decisions, or objectives. The arcs reveal probabilistic dependence of the uncertain quantities and information available at the time of the decisions.
Ross D. Shachter, C. Robert Kenley
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Belief influence diagrams

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), 2014
Influence diagrams are one of the most effective representational tools for decision analysis. However, probabilistic influence diagrams require the availability of probability distributions for all problem's uncertain variables which is not always typical to most real world applications.
Rahma Ferjani, Imen Boukhris, Zied Elouedi   +2 more
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Compiling influence diagrams

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1996
This paper examines a class of algorithms 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 manually solving time constrained decision problems.
P.E. Lehner, A. Sadigh
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Influence Diagram Retrospective

Decision Analysis, 2005
Since the invention of Influence diagrams in the mid-1970s, they have become a ubiquitous tool for representing uncertain situations. This single diagram replaced awkward manipulations of decision trees and nature’s trees with a single representation that displays both the sequential and informational structure of decisions.
Ronald A. Howard, James E. Matheson
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