Results 211 to 220 of about 139,233 (252)
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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.
Paul E. Lehner, Azar Sadigh
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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.
Paul E. Lehner, Azar Sadigh
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Decomposition of influence diagrams
Journal of Applied Non-Classical Logics, 2001When 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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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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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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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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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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Decomposable Probabilistic Influence Diagrams
Probability in the Engineering and Informational Sciences, 1991Probabilistic 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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Probabilistic Inference and Influence Diagrams
Operations Research, 1988An 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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Interactive dynamic influence diagrams
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, 2007Partially 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 J. Gmytrasiewicz
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Comment on Influence Diagram Retrospective
Decision Analysis, 2006Influence diagrams were first used in 1973 as a way to model political conflicts in the Persian Gulf and measure the value of information collected by the Defense Intelligence Agency. The number of scenarios for events in the region was too large to be represented as a conventional decision tree model.
Ronald A. Howard +4 more
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Dynamic programming and influence diagrams
IEEE Transactions on Systems, Man, and Cybernetics, 1990The concept of a super value node is developed to extend the theory of influence diagrams to allow dynamic programming to be performed within this graphical modeling framework. The operations necessary to exploit the presence of these nodes and efficiently analyze the models are developed.
Joseph A. Tatman, Ross D. Shachter
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Sensitivity analysis in influence diagrams
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2003The influence diagram framework serves as a powerful modeling tool for symmetric decision problems with a single decision maker. However, one of the main difficulties when representing decision problems using influence diagrams is eliciting the utilities and the probabilities.
Thomas D. Nielsen, Finn Verner Jensen
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