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

生物有机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，因为受到挑战的有机体会产生抗药性。希望能够利用进化和适应来达到治疗和技术目标。例如，通过一系列适当的操作，我们能否让一群不同种类的癌细胞进化为良性癌细胞？或者，我们可以引导人口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毒素的细菌吗？PI建议使用计算博弈论方法来实现这一点。生物对手有一个可以利用的明显弱点：进化和适应是短视的--它不会在游戏树上向前看。PI建议开发技术，以利用在不完全信息游戏中无法向前看的对手。该项目将通过计算利用疾病的近视进化/适应，为在广泛的背景下与疾病作斗争的对手利用方法奠定基础。这基本上适用于无限数量的疾病(在种群、个体和药物设计水平上)，适用于合成生物学(不插入外来遗传物质)，并适用于提出关于我们引导进化/适应能力的基本问题。因此，这项工作有可能为产生异常广泛的影响铺平道路。除了培训和指导参与该项目的博士生外，这项拟议的工作还具有重大的教育影响。这位PI将把拟议研究的一些最重要的成果纳入他的博士级课程、高级人工智能和电子市场基础。他还建议就这些主题进行演讲和教程。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，因为受到挑战的有机体会产生抗药性。希望能够利用进化和适应来达到治疗和技术目标。例如，通过一系列适当的操作，我们能否让一群不同种类的癌细胞进化为良性癌细胞？或者，我们可以引导人口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毒素的细菌吗？PI建议使用计算博弈论方法来实现这一点。生物对手有一个可以利用的明显弱点：进化和适应是短视的--它不会在游戏树上向前看。PI建议开发技术，以利用在不完全信息游戏中无法向前看的对手。这项拟议的工作有三个智力焦点。首先，扩展了克罗尔和桑德霍尔姆的IJCAI-15论文，以处理近视对手的节点评估不完全已知的情况，而是具有不确定性。第二，开发利用对手近视的游戏抽象技术。第三，开发在对手是群体(例如，细胞)而不是个人的情况下易于处理的游戏表示。

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.