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

* 计算方法和计算技术已被广泛用于解决具有科学和社会意义的问题。 虽然基于这些技术的软件系统是用创新的想法设计的（通常是在复杂的数学框架下），以提高效率并最好地利用现有数据，但解释这些系统产生的解决方案的含义并证明其合理性变得越来越具有挑战性。众所周知，在网络科学中缺乏原则性的方法来划分网络社区，在人工智能（AI）中缺乏处理公平或其他社会问题的方法。我同意这样的建议，即理解这些系统和基本方法需要“概念和方法范式的转变”，我认为关键在于发展解决方案概念，抓住问题的内在特征和基本现象。为了服务于我的长期目标，即为人工智能，网络科学和相关领域的问题开发数学上合理，计算效率高，对社会负责的计算方法和建模工具，拟议的五年计划旨在通过专注于图论，概率，以及现有解决方案概念的算法表征和可以直接使用或在实践中作为参考的新解决方案概念的设计。利用计算机科学、图论和概率论的工具，研究将沿着沿着三条调查路线进行，处理（I）解决方案概念，这些概念概括、放松或专门化人工智能推理、算法决策和网络分析中问题的标准解决方案概念;（2）复杂网络的群落结构和其他中尺度组织，特别是，从网络实体的高阶交互中出现的，可以用图论属性或功能/社会约束来表征的那些;以及（III）具有特殊性质的子图，这些子图可以用作与网络上的动态过程相关的问题的解概念。预计这项研究将大大提高我们对人工智能推理，算法决策和网络分析中几类重要问题的计算和社会方面的知识。它在数据丰富的领域具有实际意义，在这些领域，计算和网络视角已经变得不可或缺。我们对解决方案概念的描述将提供一个独特的视角，以帮助实现开发高效和强大的推理，学习和数据分析系统的目标，这些系统与人类的社会和伦理价值观保持一致。 通过研究开发的数学思想和数学模型可以被研究人员/从业人员用来开发软件系统，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。

***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.**