Information Theory for Distributed AI (INFORMED-AI)

人工智能（AI）即将以影响我们日常生活的方式广泛部署。它可能以自动驾驶汽车或导航系统的形式实现这一目标，导航系统可以根据实时交通信息优化路线。它可以通过智能家居来实现这一点，在智能家居中，高功率设备的使用是根据可再生能源发电的真实的预测智能地定时的。它可以通过在发生重大事故（自然或人为）时自动协调应急车辆，或者通过协调集体从事某些任务（如搜索和救援）的小型机器人群来实现这一点。迄今为止，大部分关于人工智能的研究都集中在优化单个智能体执行单个指定任务的性能上。到目前为止，很少有工作的紧急性质的系统中部署了大量这样的代理，以及由此产生的相互作用。这种相互作用最终可能会扰乱代理人已经优化的环境。例如，如果大量自动驾驶汽车根据实时信息同时选择同一条路线，那么这条路线上的道路可能会超载。如果大量智能家居同时开启设备以应对风能发电量的增加，可能会破坏电网的稳定。如果大量股票交易算法代理对新信息做出类似的反应，可能会破坏金融市场的稳定。因此，自主代理之间的互动的紧急影响不可避免地改变他们的操作环境，提高了重要的基础设施网络的可预测性和鲁棒性的关注。与此同时，它们提供了优化分布式人工智能系统的前景，以利用合作、信息共享和集体学习。因此，未来的关键挑战是设计交互式AI的分布式系统，这些系统可以利用集体行为中的协同作用，同时对不必要的紧急影响具有弹性。生物进化已经解决了许多这样的挑战，蚂蚁和蜜蜂等社会性昆虫就是一个例子，它们从非常简单的个体行为中，在殖民地水平上产生了高度复杂和适应良好的反应！该项目的目标是发展数学基础，用于理解和利用由相对简单的代理组成的复杂系统的紧急特征。虽然已经有相当多的研究，这些问题，这个项目的新奇是在使用信息理论来研究基本的数学限制在这样的系统中的学习和优化。信息论是数学的一个分支，非常适合解决这些问题。从这项研究的见解将被用来通知人工代理在由大量的交互代理的环境中运行的新算法的发展。该项目将汇集信息理论，网络科学和复杂系统的数学家，以及机器学习，人工智能和机器人技术的工程师和计算机科学家。目标是将理论见解转化为部署在真实的世界应用程序真实的系统上的算法;从部署和测试交互系统中的算法中吸取的经验教训将用于在良性循环中改进模型和算法。

Artificial intelligence (AI) is on the verge of widespread deployment in ways that will impact our everyday lives. It might do so in the form of self-driving cars or of navigation systems optimising routes on the basis of real-time traffic information. It might do so through smart homes, in which usage of high-power devices is timed intelligently based on real- time forecasts of renewable generation. It might do so by automatically coordinating emergency vehicles in the event of a major incident, natural or man-made, or by coordinating swarms of small robots collectively engaged in some task, such as search-and-rescue. Much of the research on AI to date has focused on optimising the performance of a single agent carrying out a single well-specified task. There has been little work so far on emergent properties of systems in which large numbers of such agents are deployed, and the resulting interactions. Such interactions could end up disturbing the environments for which the agents have been optimised. For instance, if a large number of self-driving cars simultaneously choose the same route based on real-time information, it could overload roads on that route. If a large number of smart homes simultaneously switch devices on in response to an increase in wind energy generation, it could destabilise the power grid. If a large number of stock-trading algorithmic agents respond similarly to new information, it could destabilise financial markets. Thus, the emergent effects of interactions between autonomous agents inevitably modify their operating environment, raising significant concerns about the predictability and robustness of critical infrastructure networks. At the same time, they offer the prospect of optimising distributed AI systems to take advantage of cooperation, information sharing, and collective learning. The key future challenge is therefore to design distributed systems of interacting AIs that can exploit synergies in collective behaviour, while being resilient to unwanted emergent effects. Biological evolution has addressed many such challenges, with social insects such as ants and bees being an example of highly complex and well-adapted responses emerging at the colony level from the actions of very simple individual agents! The goal of this project is to develop the mathematical foundations for understanding and exploiting the emergent features of complex systems composed of relatively simple agents. While there has already been considerable research on such problems, the novelty of this project is in the use of information theory to study fundamental mathematical limits on learning and optimisation in such systems. Information theory is a branch of mathematics that is ideally suited to address such questions. Insights from this study will be used to inform the development of new algorithms for artificial agents operating in environments composed of large numbers of interacting agents. The project will bring together mathematicians working in information theory, network science and complex systems with engineers and computer scientists working on machine learning, AI and robotics. The aim goal is to translate theoretical insights into algorithms that are deployed onreal world applications real systems; lessons learned from deploying and testing the algorithms in interacting systems will be used to refine models and algorithms in a virtuous circle.