CAREER: Adversarial Artificial Intelligence for Social Good

人工智能技术的成功导致了它们的广泛部署，在不确定性下进行推理的算法，如机器学习，具有特别高的影响。 然而，一个经常被忽视的挑战是许多领域的对抗性，在这些领域中，社会、经济和政治利益可能试图操纵智能系统犯下代价高昂的错误。 虽然人工智能在玩对抗性游戏（如国际象棋和扑克）方面有着悠久的历史，但这些方法并不适合许多现实世界的情况。 拟议研究的目标是为对抗性人工智能开发一个范围和适用性更广的通用框架，建立在博弈论，人工智能规划和网络安全的见解基础上。拟议研究的一个关键建模见解是，在广泛的设置中的攻击可以建模为规划问题，因此鲁棒算法可以从根本上被视为拦截攻击计划。 我们的研究将开发新的基础技术，可扩展的计划拦截下的不确定性，建立了Stackelberg游戏的框架。所提出的技术将利用抽象、状态的因子化表示和值函数近似的组合。 此外，新的可扩展算法将开发多阶段的阻断问题，建模为顺序随机游戏，考虑完美和不完美的信息。此外，该研究将在多防御者和多攻击者封锁博弈中做出新的建模和算法贡献。 最后，在更应用的竞技场中，这项研究将在将对抗性人工智能的进步应用于表现出重要对抗性方面的模型问题方面做出重大的智力贡献，例如隐私保护数据共享，访问控制和审计政策以及疫苗设计。

The success of AI technologies has resulted in their widespread deployment, with algorithms for reasoning under uncertainty, such as machine learning, having a particularly high impact. A challenge that is often ignored, however, is the adversarial nature of many domains, in which social, economic, and political interests may try to manipulate intelligent systems into making costly mistakes. While AI has a long history in playing adversarial games, such as chess and poker, the approaches have not been appropriate for many real-world situations. The goal of the proposed research is to develop a general framework for adversarial AI that is far broader in scope and applicability, building on insights from game theory, AI planning, and cybersecurity.A key modeling insight of the proposed research is that attacks across a broad array of settings can be modeled as planning problems, so that robust algorithms can be fundamentally viewed as interdicting attack plans. Our research will develop new foundational techniques for scalable plan interdiction under uncertainty, building off of the framework of Stackelberg games. Proposed techniques will leverage a combination of abstraction, factored representation of state, and value function approximation. In addition, novel scalable algorithms will be developed for multi-stage interdiction problems, modeled as sequential stochastic games, considering both perfect and imperfect information. Moreover, the research will make novel modeling and algorithmic contributions in multi-defender and multi-attacker interdiction games. Finally, in the more applied arena, the research will make significant intellectual contributions in applying advances in adversarial AI to model problems exhibiting important adversarial aspects, such as privacy-preserving data sharing, access control and audit policies, and vaccine design.