Multi-Criteria Intelligent Decision Making Approaches and Applications

多准则智能决策方法及应用

• 批准号: RGPIN-2017-06034
• 负责人: Yao, JingTao
• 金额: $ 1.68万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749957
• 关键词: Multi; Criteria; Intelligent; Decision; Making

Two major challenges remain in many decision-making problems, too many choices and contradictive criteria. It gets worse with large, complex, unstructured, and incomplete data. The proposed research program aims to utilize machine learning methods such as rough sets, game theory and granular computing for complex and critical decision makings. In particular, I will examine ternary decision-makings when multi-criteria are involved. Applications will be drawn from intrusion detection, financial prediction, and cancer diagnoses. The first part is to study the issues with too many choices. Rough set theory, especially probabilistic rough sets, is one of the techniques for ternary or three-way decision-makings. In real applications, one may want to reduce the number of uncertain rules to make more informed decisions. A possible solution is to lower the expectation by considering some uncertain rules as certain rules. However, this may decrease rule accuracy level and result in unacceptable consequences. Finding a proper level of trade-off is the challenge. I will examine a few probabilistic rough set models with Gini index and genetic algorithms to resolve such a problem. The key idea of these models is to use different mechanisms for evaluating certain decision rules and uncertain decision rules for an intelligent decision-making.The second part is to examine cases when some selection criteria are contradiction. For example, generality and accuracy are two contradictive criteria. I will apply game theory to resolve the dilemma by considering measures as players to seek for balanced positions that meet the needs of these criteria. Mechanisms to find equilibriums or thresholds in games will be examined. With the preliminary study on game-theoretic rough sets, I will further examine theory aspect of the new model, and apply it to complex decision problems. Competitive and cooperative games will be examined. This will lead to a study on game-theoretic learning. The third part is to examine multiple agents or multiple measures decision-makings. Majority, committee, and unanimous decisions are some of traditionally used strategies. Granular computing as well as game theory will be used to reach an intelligent consensus. I will apply game theory to deal with competitive or cooperative measures and granular computing to make a decision by considering different aspects of the problem. Possible applications of this method will be feature selection and Web-based decision support systems. In summary, I will study the possibility of building an intelligent system that assistant human to make informed and wise decisions on complex problems involving multi-criteria and multiple agents. It is hoped that this will broaden our knowledge on decision support mechanisms.

许多决策问题仍然存在两大挑战：选择过多和标准相互矛盾。对于大量、复杂、非结构化和不完整的数据，情况会变得更糟。拟议的研究计划旨在利用粗糙集、博弈论和粒度计算等机器学习方法来进行复杂且关键的决策。特别是，我将研究涉及多标准时的三元决策。应用程序将来自入侵检测、金融预测和癌症诊断。 第一部分研究选择过多的问题。粗糙集理论，尤其是概率粗糙集，是三元或三向决策的技术之一。在实际应用中，人们可能希望减少不确定规则的数量以做出更明智的决策。一种可能的解决方案是通过将一些不确定的规则视为确定的规则来降低期望。然而，这可能会降低规则的准确性水平并导致不可接受的后果。找到适当的权衡水平是一个挑战。 我将研究一些带有基尼指数和遗传算法的概率粗糙集模型来解决这样的问题。 这些模型的核心思想是使用不同的机制来评估某些决策规则和不确定的决策规则，以实现智能决策。第二部分是检查某些选择标准相互矛盾的情况。 例如，通用性和准确性是两个相互矛盾的标准。我将运用博弈论来解决这个困境，作为参与者考虑采取措施，寻求满足这些标准需要的平衡位置。将研究在博弈中寻找均衡或阈值的机制。 通过对博弈论粗糙集的初步研究，我将进一步研究新模型的理论方面，并将其应用于复杂的决策问题。将审查竞争性和合作性游戏。这将导致对博弈论学习的研究。第三部分是考察多主体或多措施决策。多数、委员会和一致决定是一些传统使用的策略。 粒度计算和博弈论将用于达成智能共识。我将应用博弈论来处理竞争或合作措施以及粒度计算，以通过考虑问题的不同方面来做出决策。该方法的可能应用是特征选择和基于网络的决策支持系统。总之，我将研究建立一个智能系统的可能性，该系统可以帮助人类对涉及多标准和多个代理的复杂问题做出明智的决策。希望这将扩大我们对决策支持机制的了解。