EAGER: Exploiting a myopic opponent in imperfect-information games: Toward medical applications

EAGER：在不完美信息游戏中利用短视的对手：迈向医疗应用

• 批准号: 1546752
• 负责人: Tuomas Sandholm
• 金额: $ 10万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546752&HistoricalAwards=false
• 关键词: EAGER; Exploiting; myopic; opponent; imperfect

Living organisms adapt to challenges through evolution and adaptation. These survival mechanisms have proven to be a key difficulty in developing therapies, since the challenged organisms develop resistance. It would be desirable to be able to harness evolution and adaptation for therapeutic and technological goals. For example, through a sequence of appropriate manipulations, could we get a heterogeneous population of cancer cells to evolve to benign ones? Or, could we steer the evolution of the population to a state where we can destroy it? Could we evolve bacteria that eat toxins from the environment? The PI proposes this to be done using computational game theory approaches. Biological opponents have a distinct weakness that can be exploited: evolution and adaptation is myopic - it does not look ahead in the game tree. The PI proposes to develop techniques for exploiting opponents that cannot look ahead in imperfect-information games. This project will set the stage for opponent-exploitation approaches to battling diseases in a broad range of context by computationally exploiting the diseases' myopic evolution/adaptation. This applies to basically an unlimited number of diseases (at the population, individual, and drug design levels), to synthetic biology (without inserting foreign genetic material), and to asking fundamental questions in our ability to steer evolution/adaptation. The work thus has the potential to pave the way for exceptionally broad impact. The proposed work has significant educational impact as well, beyond training and mentoring a PhD student working on this project. The PI will incorporate some of the most important results from the proposed research into his PhD-level courses Advanced AI and Foundations of Electronic Marketplaces. He also proposes to give talks and tutorials on these topics.Living organisms adapt to challenges through evolution and adaptation. These survival mechanisms have proven to be a key difficulty in developing therapies, since the challenged organisms develop resistance. It would be desirable to be able to harness evolution and adaptation for therapeutic and technological goals. For example, through a sequence of appropriate manipulations, could we get a heterogeneous population of cancer cells to evolve to benign ones? Or, could we steer the evolution of the population to a state where we can destroy it? Could we evolve bacteria that eat toxins from the environment? The PI proposes this to be done using computational game theory approaches. Biological opponents have a distinct weakness that can be exploited: evolution and adaptation is myopic - it does not look ahead in the game tree. The PI proposes to develop techniques for exploiting opponents that cannot look ahead in imperfect-information games. The proposed work has three intellectual foci. First, extending an IJCAI-15 paper by Kroer and Sandholm to handle the setting where the myopic opponent's node evaluation is not known exactly, but rather with uncertainty. Second, developing game abstraction techniques that leverage the opponent's myopia. Third, developing game representations that are tractable in settings where the opponent is a population (e.g., of cells) rather than an individual.

生物通过进化和适应来适应挑战。这些生存机制已被证明是开发治疗方法的关键困难，因为受挑战的生物体会产生耐药性。希望能够利用进化和适应来实现治疗和技术目标。例如，通过一系列适当的操作，我们是否可以让一群异质的癌细胞进化成良性细胞？或者，我们可以引导种群的进化到一个我们可以摧毁它的状态吗？我们能进化出吃环境中毒素的细菌吗？PI建议使用计算博弈论方法来完成这一点。生物学上的对手有一个明显的弱点可以利用：进化和适应是短视的--它没有在游戏树中向前看。PI建议开发技术，以利用对手，不能预见未来的信息游戏。 该项目将通过计算利用疾病的近视进化/适应，为在广泛的背景下对抗疾病的主动开发方法奠定基础。这基本上适用于无限数量的疾病（在人口，个人和药物设计层面），合成生物学（不插入外来遗传物质），以及对我们引导进化/适应能力的基本问题。因此，这项工作有可能为产生特别广泛的影响铺平道路。拟议的工作具有重大的教育影响，以及超越培训和指导博士生在这个项目上工作。PI将把拟议研究中的一些最重要的成果纳入他的博士课程高级人工智能和电子市场基础。他还建议就这些主题进行讲座和辅导。生物体通过进化和适应来适应挑战。这些生存机制已被证明是开发治疗方法的关键困难，因为受挑战的生物体会产生耐药性。希望能够利用进化和适应来实现治疗和技术目标。例如，通过一系列适当的操作，我们是否可以让一群异质的癌细胞进化成良性细胞？或者，我们可以引导种群的进化到一个我们可以摧毁它的状态吗？我们能进化出吃环境中毒素的细菌吗？PI建议使用计算博弈论方法来完成这一点。生物学上的对手有一个明显的弱点可以利用：进化和适应是短视的--它没有在游戏树中向前看。PI建议开发技术，以利用对手，不能预见未来的信息游戏。拟议的工作有三个知识焦点。首先，扩展Kroer和Sandholm的IJCAI-15论文，以处理近视对手的节点评估不确切，而是不确定的设置。第二，开发游戏抽象技术，利用对手的近视。第三，开发在对手是一个群体的环境中易于处理的游戏表示（例如，而不是一个人。