RI: Nonpropositional Nonmonotonic Languages for Knowledge Representation

知识表示形式主义是精确定义的语言，用于表达声明性知识（断言），就像典型的编程语言用于表达过程性知识（算法）一样。由于陈述性知识在智能行为中的重要作用，知识表示理论是人工智能的一个重要组成部分。非单调的知识表示语言是特别有价值的，因为它们允许对默认和异常进行推理。非单调知识表示的研究正变得越来越多的实验和应用，与出现的高效的答案集求解器-软件系统计算稳定model.The项目的目标是重新制定变量的语义在非单调逻辑的方式，这将使这一理论更接近现实的国家的最先进的实现。这将使用一个新的定义，一个稳定的模型，是基于翻译的逻辑程序与变量到经典逻辑。这项工作的目的是澄清和简化的语义的几个知识表示语言，用于人工智能的应用，许多领域的科学和技术。

ABSTRACTKnowledge representation formalisms are precisely defined languages designed for expressing declarative knowledge (assertions), just as typical programming languages are used to express procedural knowledge (algorithms). Because of the important role of declarative knowledge in intelligent behavior, the theory of knowledge representation is a key part of artificial intelligence. Nonmonotonic knowledge representation languages are particularly valuable in view of the fact that they allow reasoning about defaults and exceptions. Research on nonmonotonic knowledge representation is becoming increasingly experimental and applied, in connection with the emergence of efficient answer set solvers--software systems for computing stable models.The goal of this project is to reformulate the semantics of variables in nonmonotonic logic in a way that will bring this theory closer to the reality of state-of-the-art implementations. This will be achieved using a new definition of a stable model that is based on translating logic programs with variables into classical logic. This work seeks to clarify and simplify the semantics of several knowledge representation languages that are used in applications of artificial intelligence to many areas of science and technology.