Information Theory for Distributed AI (INFORMED-AI)

人工智能(AI)即将广泛部署，将影响我们的日常生活。它可能会以自动驾驶汽车或导航系统的形式做到这一点，导航系统根据实时交通信息优化路线。它可能会通过智能家居来做到这一点，在智能家居中，大功率设备的使用是根据对可再生能源发电量的实时预测进行智能计时的。它可以通过在发生重大事故时自动协调应急车辆来做到这一点，无论是自然的还是人为的，或者通过协调成群结队的小型机器人共同执行某些任务，如搜救。到目前为止，关于人工智能的大部分研究都集中在优化单一代理执行单一特定任务的性能上。到目前为止，关于部署了大量这样的代理的系统的紧急性质以及由此产生的相互作用的工作很少。这种相互作用可能最终会扰乱这些代理人已被优化的环境。例如，如果大量自动驾驶汽车根据实时信息同时选择同一路线，可能会使该路线上的道路超载。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，自治代理之间交互的紧急影响不可避免地改变了它们的操作环境，引起了人们对关键基础设施网络的可预测性和稳健性的极大担忧。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分布式的互动人工智能系统，该系统能够在集体行为中利用协同效应，同时对意外的突发影响具有弹性。生物进化已经解决了许多这样的挑战，蚂蚁和蜜蜂等群居昆虫就是一个例子，从非常简单的个体代理人的行动中，在群体层面上出现了高度复杂和适应良好的反应！这个项目的目标是为理解和利用由相对简单的代理人组成的复杂系统的涌现特征发展数学基础。虽然对这类问题已经有了相当多的研究，但这个项目的新颖性在于使用信息论来研究在这类系统中学习和优化的基本数学限制。信息论是数学的一个分支，非常适合解决这样的问题。这项研究的见解将被用于开发在由大量相互作用的代理组成的环境中操作的人工代理的新算法。该项目将汇集信息论、网络科学和复杂系统领域的数学家，以及机器学习、人工智能和机器人领域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现实世界应用程序真实系统上的算法；从在交互系统中部署和测试算法中学到的经验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.