人工智能是一门多学科综合的前沿学科，它由计算机科学、控制论、信息论、神经生理学、心理学、语言学等相互渗透交叉。 人工智能研究如何用计算机来表示和执行人类的智能活动，以模拟人脑所从事的推理、学习等功能，并解决需要人类智力的问题求解以及图像识别、自然语言理解、演化和优化算法。 智能原理与系统课程涵盖了人工智能的典型和现代智能体的内容，同时更强调实践系统和应用。核心是基于符号逻辑的搜索算法，知识表示，推理及其规划，智能体的结构、模型及系统设计。主要教学内容包括：（1） 问题求解中的状态空间表示，盲目搜索，启发搜索及最优搜索。（2） 知识及其表示的方法，了解知识维护、知识噪声和知识挖掘的基本概念和方法。（3） 智能推理中的单调和非单调推理，归结原理及其正向、逆向和双向演绎。（4） 规划的理论和方法、基于示例的机器学习。（5） 智能体设计及其应用范例。本课程将通过理论教学和计算机上机实践，使学生掌握问题求解的一般方法及其在智能计算、智能系统中的基础理论和方法。

Artificial Intelligence is a Multi-discipline science. It’s developing by penetrating each other include computer science, control theory, information theory, neural physiology, psychology, philology, etc. AI researches how to representing and carrying out human intelligent activity, simulating brain execute reasoning, learning and programming, solving complex problems that only be processed by human, such as image recognition, nature language understanding, evolvement and optimize algorithm. Intelligent principle and system contains classic content and modern agent of AI while it emphasizes on practice system and application. The cores of intelligent principle and system are search algorithms based on symbolic logic, knowledge representation, reasoning and planning, the structure and model, the techniques and methods of intelligent agent. Main teaching contents include: (1) State space representation, blindness search, heuristic search and best-first search in problem-solving. (2) Knowledge and methods of its representation, understanded basic concept and measure of knowledge maintenance, knowledge noise and knowledge discovery. (3) monotony and non-monotony reasoning, resolution principle and forward, backward and bi-deduction in intelligent deduction. (4) theory and methods of planning, machine learning based on examples. (5) design methods and typical applied examples of intelligent agent. This course includes theory teaching and computer experiment that makes students mastering the basic methods of problem-solving and developing ability to intelligent computing and intelligent system