Teaching Artificial Intelligence and Expert Systems Courses:Content and Strategies for Undergraduate Faculty

教授人工智能和专家系统课程：本科教师的内容和策略

• 批准号: 8954352
• 负责人: Oscar Garcia
• 金额: $ 12.95万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-11-01 至 1991-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8954352&HistoricalAwards=false
• 关键词: Teaching; Artificial; Intelligence; Expert; Systems

In order for undergraduate teaching faculty to be capable and comfortable in introducing courses in artificial intelligence (AT) and expert systems into the curriculum, they must be well founded in content and have personally experienced prototypical development processes in AT. In addition they need to know the associated learning strategies and experiences of those who have taught and researched this area. This project is presenting an intensive, three week, Summer Institute for 25 faculty consisting of content modules, hands-on laboratory experiments, and teaching strategy sessions to present up-to-date information on Symbolic and Logic Programming, Fundamentals of Artificial Intelligence, and Knowledge-Based and Expert Systems. The fundamental concepts of symbolic programming (as exemplified by the use of Lisp) and of classical logic and logic programming (within the practical limits of Prolog) are being presented first in both a theoretical and a practical laboratory setting. The second week is covering the fundamental paradigms of artificial intelligence (reasoning, goal reduction, constraint propagation, searching, control, deduction, knowledge representation, and the elements of machine learning, vision, robotics, natural language processing). During the third week the participants are learning knowledge engineering concepts and practicing them, working with and gaining experience in programming, analyzing, and using a number of KBS and ES shells in laboratories, and designing a working rule-based system of their choice. The highly modular nature of this summer institute is offering the attending faculty a wide variety of individually tailored courses adaptable to the precise needs and circumstances of their home institution curriculum environment.

为了使本科教学人员能够和 在介绍人工智能（AT）课程方面感到舒适， 专家系统纳入课程，他们必须有很好的基础内容 并亲身经历了原型开发过程， 原子 此外，他们还需要了解相关的学习策略 和那些谁教和研究这个领域的经验。 这个项目是一个密集的，为期三周，暑期研究所 为25名教师提供内容模块，动手实验室 实验和教学策略会议，以呈现最新的 符号与逻辑程序设计基础 人工智能、知识系统和专家系统。 符号编程的基本概念（如 使用Lisp）和经典逻辑和逻辑编程（在 Prolog的实际限制）首先在两个 理论和实际的实验室设置。 第二周是 涵盖了人工智能的基本范式 （推理，目标简化，约束传播，搜索，控制， 演绎、知识表示和机器的要素 学习、视觉、机器人、自然语言处理）。 期间 第三周，学员学习知识工程概念 并练习它们，与编程一起工作并获得编程经验， 在实验室中分析和使用许多KBS和ES外壳，以及 设计一个他们选择的基于规则的工作系统。 这个暑期学院的高度模块化性质提供了 参加各种各样的个性化定制课程的教师 适应他们的家庭的确切需要和环境 机构课程环境。