Knowledge Processing

Course Purpose
The purpose of this course is to learn well-known methods of knowledge representation, reasoning and machine learning as basic technologies for Artificial Intelligence, and to consider their possibilities and the limits of their applications.
Learning Goals
Students will learn well-known methods of knowledge repesentation, reasoning and machine learning, and gain a basic understanding of knowledge information processing.
Topic
Session 1Knowledge information processing and problem solving
Session 2Search method
Session 3Knowledge representation (1) Production system, frame representation, semantic network
Session 4Knowledge representation (2) Formal logic
Session 5Plan generation
Session 6Non-classical logic and non-monotonic reasoning (1) Non-monotonicity, default logic
Session 7Non-classical logic and non-monotonic reasoning (2) Hypothesis reasoning, abduction
Session 8Mid-term exam
Session 9Machine learning (1) Machine learning mechanism, version space method, decision tree
Session 10Machine learning (2) Reinforcement learning, statistical learning
Session 11Distributed Artificial Intelligence and Agents
Session 12Neural nets and evolutionary computation
Session 13Summary
Session 14Test
＊＊This content is based on April 1, 2024. For the latest syllabus information and details, please check the syllabus information inquiry page provided by the university.＊＊