Results 231 to 240 of about 593,613 (276)

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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Evaluating Influence Diagrams

Operations Research, 1986
An influence diagram is a graphical structure for modeling uncertain variables and decisions and explicitly revealing probabilistic dependence and the flow of information. It is an intuitive framework in which to formulate problems as perceived by decision makers and to incorporate the knowledge of experts.
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Supporting Negotiations over Influence Diagrams

Decision Analysis, 2009
We deal with issues concerning negotiation support for group decisions over influence diagrams, when the group members disagree about utility and probability assessments. We base our discussion on a modification of the balanced increment solution, which guarantees a final negotiated Pareto optimal alternative.
Rios Aliaga, Jesus, Insua, D. Rios
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Mixed Influence Diagrams

2003
This paper presents an architecture for exact evaluation of influence diagrams containing a mixture of continuous and discrete variables. The proposed architecture is the first architecture for efficient exact solution of linear-quadratic conditional Gaussian influence diagrams with an additively decomposing utility function.
Anders L. Madsen, Frank Jensen
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Interactive dynamic influence diagrams

Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, 2007
Partially Observable Markov Decision Processes (POMDPs) emerged as the primary framework for decision-theoretic planning in single agent settings. Solutions to POMDPs are optimal plans which are conditional on future observations. Dynamic Influence Diagrams (DIDs) are computational representations of POMDPs which compute solutions for finite time ...
Kyle Polich, Piotr Gmytrasiewicz
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Probabilistic Inference and Influence Diagrams

Operations Research, 1988
An influence diagram is a network representation for probabilistic and decision analysis models. The nodes correspond to variables which can be constants, uncertain quantities, decisions, or objectives. The arcs reveal the probabilistic dependence of the uncertain quantities and the information available at the time of the decisions. The detailed data
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Extended Influence Diagram Generation

2007
One step towards achieving high interoperability is to get an understanding of the current degree of interoperability, which calls for interoperability analyses. Extended influence diagrams have been proposed as an approach for conducting such interoperability analyses [1].
R. Lagerström, P. Johnson, P. Närman
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Influence Diagrams:

Medical Decision Making, 1997
Mark Helfand, Stephen G. Pauker
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