EAGER: Self-Assembly of Complex Systems

知识价值：自组装是描述单个组件如何通过局部相互作用来排列自身以形成有组织结构的模型。它最初是作为纳米技术构建的模型。复杂系统理论描述了自然界中的许多现象，从人类智能和进化的生物群落到人类社会网络、经济和文化。在这些系统中，复杂性以一种无法从对组成部分及其之间关系的理解中立即显现出来的方式出现。在这个项目中，自组装将作为复杂系统创建的一种机制进行研究。自组装可以证明是等效的其他模型的复杂系统。此外，它可以像计算机一样编程。该项目的目标是开发有效的方法来编程自组装，以产生具有实际应用价值的有趣的复杂系统。作为开始，我们已经开发了自组装到图上的映射，使我们能够使用有效的算法来确定所构建的系统。因此，自组装应该能够产生复杂的系统，并提供这些类型的系统的有效和现实的模拟。在该项目中，自组装算法将应用于游戏的自动内容生成，其中自组装自动创建游戏玩家与之交互的情境和非玩家角色。我们的猜想是，这将提供更多动态和现实的游戏环境，而且，这将是一个有趣的测试平台，用于研究自组装和复杂系统之间的关系。更广泛的影响：本研究整合了化学、物理学、生物学和计算机科学的思想，将自组装与复杂系统联系起来，并产生潜在的变革性工具，这些工具不仅可以提高对复杂系统的理解，还可以为各种应用领域的创新复杂系统奠定基础。这些领域包括纳米技术、人工智能、艺术、文学和电脑游戏。有许多自然现象（如人类智能、生命系统），传统的符号计算模型只能捕捉到它们基本能力和特征的一部分。人类语言就是一个例子。可以想象，这项研究可能会产生能够产生目标系统的软件，这些目标系统可以捕获在自然界中观察到的一些能力、适应性和复杂性。如果成功，该项目可能会通过计算机程序为现实和复杂的行为提供一个新的范例，并且不仅可能影响纳米技术，还可能影响需要自动生成现实内容的应用。此外，我们的自组装模型可以以易于处理的方式生成这些内容。此外，在研究者的指导下，研究生和本科生将在这个项目中组成一个团队，并将在已经提出的独特的多学科方法中进行教育。

Abstract for EAGER: Self-Assembly of Complex Systems Intellectual Merit: Self-assembly is a model for how individual components arrange themselves through local interactions to form organized structures. It originated as a model for construction of nanotechnology. The theory of complex systems describes many phenomena in nature, from human intelligence and evolving communities of organisms to human social networks, economies, and cultures. In these systems, complexity emerges in ways that is not immediately obvious from an understanding of the component parts and the relationships between them. In this project, self-assembly will be investigated as a mechanism for creation of complex systems. Self-assembly can be shown to be equivalent to other models of complex systems. In addition, it can be programmed like a computer. A goal of this project is to develop efficient ways to program self-assembly to produce interesting complex systems, which have practical applications. As a beginning to this, we have developed a mapping of self-assembly onto graphs that enables us to use an efficient algorithm to determine the system that is constructed. Thus, self-assembly should be able to generate complex systems and to provide efficient and realistic simulation of those types of systems. In the project, the self-assembly algorithms will be applied to automatic content generation for games, in which the self-assembly automatically creates situations and non-player characters with which players of the game interact. The conjecture is that this will provide more dynamic and realistic game environments, and moreover, will be an interesting test-bed for investigation of the relationship between self-assembly and complex systems.Broader Impacts: This research integrates ideas from chemistry, physics, biology, and computer science to relate self-assembly to complex systems, and to produce potentially transformative tools that will not only improve understanding of complex systems, but also form the basis for innovative complex systems in a variety of application domains. These include nanotechnology, artificial intelligence, art, literature, and computer games. There are many natural phenomena (i.e. human intelligence, living systems) for which traditional symbolic models of computation are only able to capture a part of their essential capabilities and characteristics. Human language is an example. This research conceivably could result in software that is able to produce target systems that capture some of the capability, adaptability, and complexity that is observed in nature. If successful, the project could result in a new paradigm for realistic and complex behavior through computer programs, and would potentially impact not only nanotechnology, but also applications that require automatic generation of realistic content. Moreover, our models of self-assembly can generate this content in tractable ways. In addition, under the direction of the investigator, graduate and undergraduate students will work together in a team on this project, and will be educated in the unique multidisciplinary approach that has been proposed.