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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679347
• 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.

在许多决策问题中仍然存在两大挑战，即选择太多和标准相互矛盾。对于大型、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机器学习方法，如粗糙集，博弈论和粒度计算的复杂和关键的决策。特别是，我将研究三元决策时，涉及多标准。应用程序将从入侵检测，财务预测和癌症诊断中提取。 * 第一部分是研究选择过多的问题。粗糙集理论，特别是概率粗糙集，是一种三元或三元决策方法。在真实的应用中，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，这可能会降低规则准确性水平并导致不可接受的后果。找到一个适当的权衡水平是一个挑战。 我将研究一些概率粗糙集模型与基尼指数和遗传算法来解决这样的问题。 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决策规则，以实现智能决策。*第二部分是对某些选择标准存在矛盾时的案例进行考察。 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一困境，将措施视为参与者，以寻求满足这些标准需要的平衡位置。在游戏中寻找均衡或阈值的机制将被检查。 在对博弈论粗糙集理论进行初步研究的基础上，本文将进一步研究新模型的理论方面，并将其应用于复杂决策问题。竞争和合作的游戏将被检查。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传统上使用的一些策略。 颗粒计算以及博弈论将被用来达成智能共识。我将应用博弈论来处理竞争或合作措施，并通过考虑问题的不同方面来进行粒度计算。这种方法可能的应用将是功能选择和基于Web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，帮助人类在涉及多标准和多代理的复杂问题上做出明智的决策。希望这将扩大我们对决策支持机制的了解。