CRII: RI: Analysis and Applications of Multi-Level Games

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。随着人工智能（AI）变得越来越有能力和无处不在，AI实体更有可能发现自己在一个复杂的动态环境中运行，沿着其他智能实体，这些智能实体都有不同的目标和关于环境和彼此的完全不同的信息或信念。例子包括在线广告拍卖中的竞标者、金融市场中的算法交易代理、网络中的（网络）攻击者和防御者、以分散方式制定分散的流行病应对计划的决策者层级等等。（或者，游戏，因为他们被称为），正变得越来越重要的人工智能研究和实践。经验博弈论分析（英语：Empirical game-theoretic analysis，EGTA）是一个原则性的框架，用于对超出传统博弈论分析方法范围的博弈进行（近似）推理，因为博弈过于复杂，或者因为获取有关博弈可能玩法的信息过于昂贵。该项目力求通过构建更细粒度的模型（即，更准确的近似值）。具体地说，该项目的目的是使EGTA适应多层次的游戏模型形式：可以以有向有根树的形式表示的战略交互。可拓形式博弈（Extensive-form games，EFG）是这类博弈的一个经典例子，它捕捉了代理活动、信息披露和可能的随机事件（自然行为）中的时间模式。当前的EGTA实践在模拟器中抽象出所有这样的时间模式（例如，基于代理的模型），该模型被查询以获得关于玩家的策略组合的收益数据，但是引入了本质上是正规形式并且不反映这种模式的较粗糙的游戏模型。该项目将通过明确地将底层博弈树的特征纳入经验博弈模型本身并解决由此产生的概念和计算设计挑战，例如在模型粒度（导致更好的近似）和每次迭代计算负担之间取得平衡，从而在EGTA设计中迈出下一步。 该项目的第二阶段将寻求将从EFG的EGTA开发中获得的知识转移到其他基于树的游戏模型形式的类似处理中。该项目的进展可以显著提高我们对采用高度复杂策略（如深度强化学习算法）的交互式人工智能代理系统的理解，并反过来为此类代理的稳健设计提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).With artificial intelligence (AI) becoming progressively more competent and ubiquitous, an AI entity is more likely to find itself operating in a complex, dynamic environment along with other intelligent entities all with different goals and disparate information or beliefs about their environment and each other. Examples include bidders in an online ad auction, algorithmic trading agents in a financial market, (cyber)attackers and defenders in a network, a hierarchy of policymakers formulating a decentralized epidemic response plan in a decentralized manner, and so on. As such, game theory, the systematic study of such multi-agent strategic interactions (or, games as they are called), is becoming increasingly fundamental to AI research and practice. Empirical game-theoretic analysis (EGTA) is a principled framework for (approximately) reasoning about games that are beyond the scope of traditional, analytical game-theoretic methods either because the games are too complex or because obtaining information about possible plays of the game is too expensive. This project seeks to make fundamental methodological improvements to the EGTA framework by constructing finer- grained models (i.e., more accurate approximations) of the game under consideration than those prevalent in the state of the art.Specifically, the aim of the project is to adapt EGTA to multi-level game model forms: strategic interactions that can be represented in the form of a directed, rooted tree. Extensive-form games (EFGs) are a classic example of this class of games that capture temporal patterns in agent activity, information revelation, and possible stochastic events (acts of Nature). Current EGTA practice abstracts all such temporal patterns away in a simulator (e.g., an agent-based model) that is queried to obtain payoff data for strategy combinations over players but induces a coarser game model that is essentially normal-form and does not reflect such patterns. This project will take the next step in EGTA design by explicitly incorporating features of the underlying game tree into the empirical game model itself and tackling the resulting conceptual and computational design challenges such as striking a balance between model granularity (which leads to better approximation) and per-iteration computational burden. The second phase of the project will seek to transfer knowledge gained from development of EGTA for EFGs to similar treatments of other tree-based game model forms. Advances in this project can significantly improve our understanding of systems that comprise interacting AI agents employing highly sophisticated strategies (such as deep reinforcement learning algorithms), and in turn inform robust design of such agents.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.