Artificial Intelligence and Network Science: Solution Concepts, Graph-Theoretic Characterizations, and Their Societal Aspects

人工智能和网络科学：解决方案概念、图论特征及其社会方面

• 批准号: RGPIN-2019-04904
• 负责人: Gao, Yong
• 金额: $ 1.68万
• 依托单位: University of British Columbia
• 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=761687
• 关键词: Artificial; Intelligence; Network; Science; Solution

Computational approaches and computing techniques have been widely used to solve problems of scientific and societal significance. While software systems based on these techniques are designed with innovative ideas (and often under complicated mathematical frameworks) in order to improve efficiency and to make best use of available data, it has become increasingly challenging to interpret the meaning of the solutions produced by these systems and to justify their rationale. It is well recognized that principled approaches are lacking in Network Science to delineating network communities and in Artificial Intelligence (AI) to dealing with fairness or other societal issues. I concur with the suggestion that understanding these systems and the underlying methods requires "conceptual and methodological paradigm shifts", and I believe that the key lies in developing solution concepts that capture the intrinsic characteristics of the problems and the underlying phenomena. To serve my long-term goal of developing mathematically sound, computationally efficient, and socially responsible computing methods and modelling tools for problems in AI, Network Science, and related domains, the proposed five-year program aims to address the challenge by focusing on the graph-theoretic, probabilistic, and algorithmic characterizations of existing solution concepts and the design of new solution concepts that can be used directly or as a reference in practice. Using tools from computer science, graph theory, and theory of probability, the research will be carried out along three lines of inquiry, dealing with (I) solution concepts that generalize, relax, or specialize standard solution concepts for problems in AI reasoning, algorithmic decision making, and network analysis; (II) community structure and other meso-scale organizations of complex networks, in particular, those that emerge from higher-order interactions of network entities and can be characterized by graph-theoretic properties or functional/societal constraints; and (III) subgraphs with special properties that can be used as a solution concept for problems related to dynamic processes on networks. The research is expected to significantly advance our knowledge on the computational and societal aspects of several classes of important problems in AI reasoning, algorithmic decision making, and network analysis. It has practical implications in data-rich domains where computational and network perspectives have become indispensable. Our characterizations of solution concepts will provide a unique perspective to help achieve the goal of developing efficient and robust reasoning, learning, and data analysis systems that align well with humans' social and ethic values.  Algorithmic ideas and mathematical models developed through the research can be used by researchers/practitioners to develop software systems to analyze complex phenomena in biology, economics, healthcare, social media, and sociology.

计算方法和计算技术已被广泛用于解决科学和社会意义的问题。尽管基于这些技术的软件系统是通过创新思想（通常在复杂的数学框架下）设计的，以提高效率并充分利用可用数据，但解释这些系统产生的解决方案的含义并证明其理由是合理的，它变得越来越挑战。众所周知，网络科学缺乏主要的方法来描述网络社区和人工智能（AI）来处理公平或其他社会问题。我同意这样的建议：理解这些系统和潜在方法需要“概念和方法论范式的变化”，我相信关键在于开发解决方案概念以捕获问题的内在特征和基本现象。为了实现我的长期目标，即开发数学上的声音，计算效率和对社会负责的计算方法以及为AI，网络科学及相关领域问题的问题建模工具，拟议的五年计划旨在通过专注于图形理论，概率，概率，算法和算法的解决方案概念以及可以使用新解决方案的设计来解决挑战。使用计算机科学，图理论和概率理论的工具，将沿着三条探究线进行研究，以（i）解决方案概念，以推广，放松或专门针对AI推理，算法决策制定和网络分析的问题的标准解决方案概念； （ii）尤其是复杂网络的社区结构和其他中级组织，尤其是从网络实体的高阶相互作用中出现的，可以通过图理论属性或功能/社会约束来表征； （iii）具有特殊属性的子图，可以用作与网络动态过程有关的问题的解决方案概念。预计这项研究将大大提高我们对AI推理，算法决策和网络分析的重要问题的计算和社会方面的知识。它在计算和网络观点变得必不可少的数据域中具有实际含义。我们的解决方案概念的特征将提供一个独特的观点，以帮助实现建立高效，健壮的推理，学习和数据分析系统的目标，这些系统与人类的社会和道德价值观很好地保持一致。研究人员/从业人员可以使用通过研究开发的算法思想和数学模型来开发软件系统，以分析生物学，经济学，医疗保健，社交媒体和社会学中的复杂现象。