量子概率振幅在决策支持系统中的应用

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Computational Intelligence and Soft Computing Pub Date : 2023-12-07 DOI:10.1155/2023/5532174
S. Payandeh
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引用次数: 0

摘要

建立各种框架来管理决策系统中的不确定性已经对系统设计工程师提出了许多根本性的挑战。量子范式已经被引入决策和控制社区领域,作为这种不确定性管理的可能支持平台。本文概述了如何在文献中提出和利用量子框架,特别是概率振幅来补充两种经典的概率决策方法。第一个这样的框架是基于贝叶斯网络的,第二个是基于使用质量函数定义的Dempster-Shafer (DS)理论的一个元素。本文首先介绍了这些经典方法的总结,然后回顾了它们使用量子模型框架的初步增强。特别注意了概率振幅的概念如何在这种类量子框架的扩展中被利用。数值演练示例与每种方法的演示相结合,以便更好地演示所提出框架的扩展。主要目标是更好地定义和开发一个公共平台,以便进一步探索和试验将此替代框架作为决策支持系统的一部分。
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Applications of Quantum Probability Amplitude in Decision Support Systems
Establishing various frameworks for managing uncertainties in decision-making systems have been posing many fundamental challenges to the system design engineers. Quantum paradigm has been introduced to the area of decision and control communities as a possible supporting platform in such uncertainty management. This paper presents an overview of how a quantum framework and, in particular, probability amplitude has been proposed and utilized in the literature to complement two classical probabilistic decision-making approaches. The first such framework is based in the Bayesian network, and the second is based on an element of Dempster–Shafer (DS) theory using the definition of mass function. The paper first presents a summary of these classical approaches, followed by a review of their preliminary enhancements using the quantum model framework. Particular attention was given on how the notion of probability amplitude is utilized in such extensions to the quantum-like framework. Numerical walk-through examples are combined with the presentation of each method in order to better demonstrate the extensions of the proposed frameworks. The main objective is to better define and develop a common platform in order to further explore and experiment with this alternative framework as a part of a decision support system.
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来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
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