作者：周丽华,任长春,王丽珍
页数：120
出版社：科学出版社
出版日期：2018
ISBN：9787030316776
电子书格式：pdf/epub/txt
内容简介
影响图是不确定环境中描述复杂决策问题的图模型，它表达了变量间的依赖关系、条件独立关系和决策者的偏好信息。目前，影响图已成为一种流行的标准建模工具。然而，现实中的决策环境复杂多样，在一些应用中现有的影响图还不足以满足要求，因此有必要对它们进行扩展。周丽华、任长春、王丽珍编著的《影响图的扩展及其应用(英文版)》主要介绍影响图的扩展及应用，包括针对建模无限制决策问题、涉及连续变量的决策问题、涉及非精确变量的决策问题及多Agent决策问题等方面对影响图的扩展，以及影响图的评价算法和竞争对手私人信息的推测。影响图的扩展有助于增强影响图的表达能力，丰富影响图的类型，提高影响图的适用性，促进影响图的发展，扩大影响图的应用领域。
本书特色
影响图是不确定环境中描述复杂决策问题的图模型，它表达了变量间的依赖关系、条件独立关系和决策者的偏好信息。目前，影响图已成为一种流行的标准建模工具。然而，现实中的决策环境复杂多样，在一些应用中现有的影响图还不足以满足要求，因此有必要对它们进行扩展。本书主要介绍影响图的扩展及应用，包括针对建模无限制决策问题、涉及连续变量的决策问题、涉及非精确变量的决策问题及多Agent 决策问题等方面对影响图的扩展，以及影响图的评价算法和竞争对手私人信息的推测。影响图的扩展有助于增强影响图的表达能力，丰富影响图的类型，提高影响图的适用性，促进影响图的发展，扩大影响图的应用领域
目录
Preface
Chapter 1 Introduction
1.1 Motivations
1.2 Outline of the Book
Chapter 2 Influence Diagrams
2.1 Introduction
2.2 Preliminary
2.3 Influence Diagrams
2.3.1 The graphical component of an ID
2.3.2 The numerical component of an ID
2.4 Conclusion
Chapter 3 The Extensions of Influence Diagrams
3.1 Introduction
3.2 Unconstrained Influence Diagrams (UIDs)
3.3 Limited Memory Influence Diagrams (LIMIDs)
3.4 Mixtures of Truncated Exponentials Influence Diagrams (MTE IDs)
3.5 Possibilistic Influence Diagrams (PIDs)
3.6 Interval-Valued Influence Diagrams (IIDs)
3.6.1 Bound-limited weak conditional interval probability
3.6.2 Influence diagrams with interval-valued parameters (BIIDs)
3.7 Game Theory-Based Influence Diagrams (GIDs)
3.7.1 Game theory
3.7.2 Game theory-based influence diagrams (GIDs)
3.8 Game IDs with Interval-Valued Parameters (GBIIDs)
3.9 Conclusion
Chapter 4 The Evaluation of Influence Diagrams
4.1 Introduction
4.2 The Concept of Evaluating IDs
4.3 Evaluating Standard IDs
4.3.1 Arc reversal (AR)
4.3.2 Variable elimination(VE)
4.3.3 Cooper’s method (CM)
4.4 Evaluating Interval-Valued IDs
4.4.1 Operations for interval-valued potentials
4.4.2 Interval arc reversal (IAR)
4.4.3 Interval variable elimination (IVE)
4.4.4 Chance variable and arc reversal elimination in IIDs by linear programming
4.4.5 Interval Cooper’s method (ICM) for evaluating BIIDs
4.5 Evaluating GIDs
4.5.1 Concepts
4.5.2 The design of the genetic algorithm
4.5.3 The algorithm for evaluating GIDs (EAGIDs)
4.6 Evaluating Game IDs with Interval-Valued Parameters (GBIIDs)
4.6.1 Solving games with interval-valued utilities
4.6.2 Computing strategy utilities for utility nodes shared by multiple decision makers
4.6.3 Selecting the strategy with maximal interval-valued expect utility
4.7 Conclusion
Chapter 5 Applications of Influence Diagrams
5.1 Finding Optimal Strategies for a Decision Maker by a BIID
5.2 Applying Influence Diagrams to Infer Rival’s Private Information
5.2.1 The type of rival’s private information
5.2.2 Inferring rival’s private information
5.2.3 Experiments
5.3 Conclusion
References
Chapter 1 Introduction
1.1 Motivations
1.2 Outline of the Book
Chapter 2 Influence Diagrams
2.1 Introduction
2.2 Preliminary
2.3 Influence Diagrams
2.3.1 The graphical component of an ID
2.3.2 The numerical component of an ID
2.4 Conclusion
Chapter 3 The Extensions of Influence Diagrams
3.1 Introduction
3.2 Unconstrained Influence Diagrams (UIDs)
3.3 Limited Memory Influence Diagrams (LIMIDs)
3.4 Mixtures of Truncated Exponentials Influence Diagrams (MTE IDs)
3.5 Possibilistic Influence Diagrams (PIDs)
3.6 Interval-Valued Influence Diagrams (IIDs)
3.6.1 Bound-limited weak conditional interval probability
3.6.2 Influence diagrams with interval-valued parameters (BIIDs)
3.7 Game Theory-Based Influence Diagrams (GIDs)
3.7.1 Game theory
3.7.2 Game theory-based influence diagrams (GIDs)
3.8 Game IDs with Interval-Valued Parameters (GBIIDs)
3.9 Conclusion
Chapter 4 The Evaluation of Influence Diagrams
4.1 Introduction
4.2 The Concept of Evaluating IDs
4.3 Evaluating Standard IDs
4.3.1 Arc reversal (AR)
4.3.2 Variable elimination(VE)
4.3.3 Cooper’s method (CM)
4.4 Evaluating Interval-Valued IDs
4.4.1 Operations for interval-valued potentials
4.4.2 Interval arc reversal (IAR)
4.4.3 Interval variable elimination (IVE)
4.4.4 Chance variable and arc reversal elimination in IIDs by linear programming
4.4.5 Interval Cooper’s method (ICM) for evaluating BIIDs
4.5 Evaluating GIDs
4.5.1 Concepts
4.5.2 The design of the genetic algorithm
4.5.3 The algorithm for evaluating GIDs (EAGIDs)
4.6 Evaluating Game IDs with Interval-Valued Parameters (GBIIDs)
4.6.1 Solving games with interval-valued utilities
4.6.2 Computing strategy utilities for utility nodes shared by multiple decision makers
4.6.3 Selecting the strategy with maximal interval-valued expect utility
4.7 Conclusion
Chapter 5 Applications of Influence Diagrams
5.1 Finding Optimal Strategies for a Decision Maker by a BIID
5.2 Applying Influence Diagrams to Infer Rival’s Private Information
5.2.1 The type of rival’s private information
5.2.2 Inferring rival’s private information
5.2.3 Experiments
5.3 Conclusion
References
