Algorithms and information processing, fundamental to biological system, are an essential aspect of many elementary physical phenomena, such as molecular self-assembly. Self-assembly system has been proved to be capable of performing many logic operations by the early work. A significant challenge related to the design of molecular information processing systems is to develop a programmable architecture that controls the states of individual molecular events. Here, a novel systematic implementation of logical inference is presented based on DNA tile assembly system. Exploiting the intrinsic programmable capability of molecular interactions, firstly a seed tile configuration is constructed to encode the input information of a logical inference problem, including all facts, all inference rules and their equivalent rules. Then, three tile assembly subsystems are discussed to fulfil the main logical deduction steps. We describe mechanisms for finding the successful solutions among the many parallel assemblies. A whole tile assembly system is established on the base of a seed configuration and three subsystems. This prototype is the first programming language to implement deduction operations based on two-dimensional DNA assembly. It is demonstrated that algorithmic DNA tile assembly system can be treated as an important way to implement logic inference, which will shed light on aspects of applications in the field of artificial intelligence in the future.

算法和信息处理是生物系统的基础，是许多基本物理现象的重要方面，例如分子自组装。自组装系统已被早期的工作证明能够执行许多逻辑运算。与分子信息处理系统设计相关的一个重大挑战是开发一种可编程架构来控制单个分子事件的状态。在这里，提出了一种基于 DNA 瓦片组装系统的逻辑推理的新系统实现。利用分子相互作用的内在可编程能力，首先构建种子瓦片配置来编码逻辑推理问题的输入信息，包括所有事实、所有推理规则及其等效规则。然后，讨论了三个瓷砖组装子系统来完成主要的逻辑推导步骤。我们描述了在许多并行程序集中找到成功解决方案的机制。在一个种子配置和三个子系统的基础上建立了一个完整的瓷砖组装系统。这个原型是第一个实现基于二维 DNA 组装的推导操作的编程语言。证明了算法DNA瓦片组装系统可以作为实现逻辑推理的重要方式，这将在未来人工智能领域的应用方面有所启发。在一个种子配置和三个子系统的基础上建立了一个完整的瓷砖组装系统。这个原型是第一个实现基于二维 DNA 组装的推导操作的编程语言。证明了算法DNA瓦片组装系统可以作为实现逻辑推理的重要方式，这将在未来人工智能领域的应用方面有所启发。在一个种子配置和三个子系统的基础上建立了一个完整的瓷砖组装系统。这个原型是第一个实现基于二维 DNA 组装的推导操作的编程语言。证明了算法DNA瓦片组装系统可以作为实现逻辑推理的重要方式，这将在未来人工智能领域的应用方面有所启发。