Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit.

计算机科学的大多数子领域都有一个接口层，应用程序通过该接口层与基础设施进行通信，这是其成功的关键（例如，网络中的互联网、数据库中的关系模型等）。到目前为止，这个接口层在人工智能中已经丢失了。一阶逻辑和概率图形模型都有一些必要的特性，但是一个可行的接口层需要将两者结合起来。马尔可夫逻辑是一种强大的新语言，它通过将权重附加到一阶公式并将其作为马尔可夫随机场特征的模板来实现这一点。大多数广泛使用的统计模型都是马尔可夫逻辑的特例，一阶逻辑是它的无限权极限。