超越计算复杂性试错动态动作与智能
Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence
摘要：
计算复杂性是计算机科学的一个核心理论，它规定了计算的难度。我们必须处理许多高度复杂的问题，对于人工智能来说尤其如此。这就提出了一个大问题：除了受计算复杂性限制之外，还有更好的方法来处理这些高度复杂的问题吗？我们相信，从智能科学的思想和方法可以应用到这些问题，并帮助我们超越计算复杂性。在本文中，我们试图澄清概念，我们提出的定义，如非特殊化计算，特殊化计算，计算代理，动态搜索。我们还提出并讨论了一个框架，即，试错+动态搜索。数划分问题是一个著名的NP完全问题，我们用这个问题作为例子来说明所讨论的思想。
英文摘要：
Computational complexity is a core theory of computer science, which dictates the degree of difficulty of computation. There are many problems with high complexity that we have to deal, which is especially true for AI. This raises a big question: Is there a better way to deal with these highly complex problems other than bounded by computational complexity? We believe that ideas and methods from intelligence science can be applied to these problems and help us to exceed computational complexity. In this paper, we try to clarify concepts, and we propose definitions such as unparticularized computing, particularized computing, computing agents, and dynamic search. We also propose and discuss a framework, i.e., trial-and-error + dynamic search. Number Partition Problem is a well-known NP-complete problem, and we use this problem as an example to illustrate the ideas discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
