SHAIC1: Towards Scalable Human-AI Coordination from First Principles

SHAIC1：从第一原则迈向可扩展的人类与人工智能协调

• 批准号: EP/Y028481/1
• 负责人: Jakob Foerster
• 金额: $ 242.77万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY028481%2F1
• 关键词: SHAIC1; Towards; Scalable; Human; AI

The goal of this proposal is to develop artificial intelligence (AI) agents, that can support and collaborate with humans in complex, real-world settings. These include, for example, industrial or service robots that can work in teams with humans and self-driving cars that interact smoothly with other traffic participants in mixed-autonomy settings. A fundamental issue is that, unlike the scalable solutions for competitive settings, current approaches for cooperative ones rely on human data and are thus limited in their scalability. Unfortunately, scaling compute to remove the need for human data is challenging in these settings. Without the well-defined objective present in zero-sum settings, it requires finding one of the few solutions that is human-compatible in a pool that also contains combinatorially many human-incompatible ones.My hypothesis is that humans have a well-defined concept of a 'good coordination solution' and to a great extent rely on this concept to solve coordination problems, i.e. when they have to work with others but cannot pre-agree on a strategy. Generally speaking, a good solution in such scenarios is one that is simple, symmetric, and therefore easy to adapt to. To move towards a formalisation and implementation of this intuitive idea, I will show how general purpose coordination policies can be efficiently discovered in complex settings using iteratively learned state-abstractions which implement simplicity and symmetry constraints.I will then robustify these policies to human sub-optimality using novel algorithms that gradually relax the constraints via online adaptation or small amounts of real-world human data.This project will result in new methods that can scale to complex human-AI coordination problems beyond the reach of the current state of the art. It will also develop a new theory that sets the scene for fundamental progress on human-AI coordination and unlocks crucial application areas, such autonomous industrial robots.

该提案的目标是开发人工智能 (AI) 代理，能够在复杂的现实环境中支持人类并与之协作。例如，其中包括可以与人类团队合作的工业或服务机器人，以及可以在混合自主环境中与其他交通参与者顺利互动的自动驾驶汽车。一个基本问题是，与竞争环境的可扩展解决方案不同，当前的合作解决方案依赖于人类数据，因此其可扩展性受到限制。不幸的是，在这些环境中，扩展计算以消除对人类数据的需求是一项挑战。如果零和环境中没有明确定义的目标，则需要在一个池中找到与人类兼容的少数解决方案之一，该池中还包含许多与人类不兼容的组合。我的假设是，人类有一个明确定义的概念“良好的协调解决方案”的概念，并在很大程度上依靠这个概念来解决协调问题，即当他们必须与他人合作但无法预先就策略达成一致时。一般来说，在这种情况下，一个好的解决方案是简单、对称、因此易于适应的解决方案。为了实现这一直观想法的形式化和实施，我将展示如何使用迭代学习的状态抽象在复杂的环境中有效地发现通用协调策略，这些状态抽象实现了简单性和对称性约束。然后我将增强这些策略以适应人类的需求。使用新颖的算法来实现最优性，这些算法通过在线适应或少量的现实世界人类数据逐渐放松约束。该项目将产生新的方法，可以扩展到超出当前技术水平的复杂的人类与人工智能协调问题。它还将开发一种新理论，为人类与人工智能协调的根本进展奠定基础，并解锁关键应用领域，例如自主工业机器人。