Approximate Solution of Interactive Dynamic Influence Diagram

2011 ◽  
Author(s):  
Jian Luo ◽  
Le Tian
Keyword(s):  
Approximate Solution ◽  
Influence Diagram ◽  
Interactive Dynamic

scholarly journals Service Innovation Decision Analysis Based on Influence Diagrams

2018 ◽  
Vol 13 (5) ◽  
pp. 895-907
Author(s):  
Liang Tan
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Directed Graph ◽  
Probabilistic Model ◽  
Service Innovation ◽  
The Other ◽  
Decision Problems ◽  
Influence Diagrams ◽  
Influence Diagram ◽  
Interactive Dynamic

The influence diagram is a probabilistic model for presenting decision problems as a directed graph. In this study, the dynamic influence diagram and the interactive dynamic influence diagram are used to model the three parties to service innovation: customers, suppliers, and service enterprises. The models analyze the decisions of these dierent parties and describe the process by which service enterprises should consider their own innovation conditions as well as those of the other parties, that is, customers and suppliers. Moreover, during the process of service innovation, service enterprises should be in constant communication with customers and suppliers. After the customers and suppliers respond, service enterprises can modify their innovation decision-making, and improve service innovation quality and income.

scholarly journals Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams

2012 ◽  
Vol 43 ◽  
pp. 211-255 ◽  
Author(s):  
Y. Zeng ◽  
P. Doshi
Keyword(s):  
Graphical Models ◽  
Exponential Growth ◽  
Model Space ◽  
Influence Diagrams ◽  
Influence Diagram ◽  
Time Step ◽  
Large Space ◽  
Equivalent Models ◽  
Conflicting Objectives ◽  
Interactive Dynamic

We focus on the problem of sequential decision making in partially observable environments shared with other agents of uncertain types having similar or conflicting objectives. This problem has been previously formalized by multiple frameworks one of which is the interactive dynamic influence diagram (I-DID), which generalizes the well-known influence diagram to the multiagent setting. I-DIDs are graphical models and may be used to compute the policy of an agent given its belief over the physical state and others' models, which changes as the agent acts and observes in the multiagent setting. As we may expect, solving I-DIDs is computationally hard. This is predominantly due to the large space of candidate models ascribed to the other agents and its exponential growth over time. We present two methods for reducing the size of the model space and stemming its exponential growth. Both these methods involve aggregating individual models into equivalence classes. Our first method groups together behaviorally equivalent models and selects only those models for updating which will result in predictive behaviors that are distinct from others in the updated model space. The second method further compacts the model space by focusing on portions of the behavioral predictions. Specifically, we cluster actionally equivalent models that prescribe identical actions at a single time step. Exactly identifying the equivalences would require us to solve all models in the initial set. We avoid this by selectively solving some of the models, thereby introducing an approximation. We discuss the error introduced by the approximation, and empirically demonstrate the improved efficiency in solving I-DIDs due to the equivalences.

scholarly journals Time-critical interactive dynamic influence diagram

2015 ◽  
Vol 57 ◽  
pp. 44-63 ◽  
Author(s):  
Yinghui Pan ◽  
Yifeng Zeng ◽  
Yanping Xiang ◽  
Le Sun ◽  
Xuefeng Chen
Keyword(s):  
Influence Diagram ◽  
Time Critical ◽  
Interactive Dynamic

Approximate Solution of One Dynamic Problem of Geoinformatics

2010 ◽  
Vol 42 (5) ◽  
pp. 1-11 ◽  
Author(s):  
Vladimir M. Bulavatskiy ◽  
Vasiliy V. Skopetsky
Keyword(s):  
Approximate Solution ◽  
Dynamic Problem

On Improved Approximate Solution of the Fredholm Integral Equation

2006 ◽  
Vol 6 (3) ◽  
pp. 264-268
Author(s):  
G. Berikelashvili ◽  
G. Karkarashvili
Keyword(s):  
Integral Equation ◽  
Approximate Solution ◽  
Fredholm Integral Equation ◽  
Algebraic Equations ◽  
Order Convergence ◽  
One Dimensional ◽  
Averaging Operator ◽  
Linear Algebraic Equations ◽  
Convergence Solution

AbstractA method of approximate solution of the linear one-dimensional Fredholm integral equation of the second kind is constructed. With the help of the Steklov averaging operator the integral equation is approximated by a system of linear algebraic equations. On the basis of the approximation used an increased order convergence solution has been obtained.

Approximate Solution Of A Singular Integral Equation With A Multiplicative Cauchy Kernel In The Half-Plane

2008 ◽  
Vol 8 (2) ◽  
pp. 143-154 ◽  
Author(s):  
P. KARCZMAREK
Keyword(s):  
Integral Equation ◽  
Approximate Solution ◽  
Singular Integral Equation ◽  
Half Plane ◽  
Singular Integral ◽  
Trigonometric Polynomials ◽  
Cauchy Kernel

AbstractIn this paper, Jacobi and trigonometric polynomials are used to con-struct the approximate solution of a singular integral equation with multiplicative Cauchy kernel in the half-plane.

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