人工知能入門
担当教員
授業の到達目標及びテーマ
Basic AI principles and techniques for beginners
授業の概要と方法
This course will present students 6 demonstration AI systems for promoting students motivations, and then explain two of them in details and let students experiencing AI programming.The rest of topics such as knowledge representation, inference mechanisms, decision making under uncertainty are given and students are requested to do work in class and home woork as well so as to understand the lecture contents well.
授業計画
| 回 | テーマ | 内容 |
|---|---|---|
| 1 | Introduction of AI and intelligent agents | What is AI? AI's history What is IA? IA's characteristics |
| 2 | 6 demonstration systems | Run and explain each demonstration system |
| 3 | Solving problems by searching | Knowledge and problem types, problem-sloving agents, example problems, Searching for solution |
| 4 | Uninformed search strategies | A number of search strategies, breadth-first, depth-firsr search |
| 5 | AI programming in Java (1) | City travelling route problem |
| 6 | Searching for decisions in games | Game problems, 2-players games strategies, minimax algorithm, alpha-beta algorithm |
| 7 | AI programming in Java (2) | Tic-Tac-Toe AI based game in Java |
| 8 | Mid-term review | Review lecture contents and explain the example solutions to work in class and home work |
| 9 | A simple logic | Propositional logic, Syntax, Semantics, |
| 10 | Reasoning in propositional logic | Wumpus world problem, logic agent, 7 inference rules, logic inference |
| 11 | First-order logic | Syntax and semantics, Atomic and complex sentences, Quantifiers, The Wumpus world in first-order logic |
| 12 | Reasoning in first-order logic | 10 inference rules, knowledge representation, unification, an example of inference in first-order logic |
| 13 | Reasoning with uncertainty | Uncertainty, the axioms of probability, Bayes rule, full joint distribution, independence |
| 14 | Bayesian networks and probabilistic reasoning | Representing knowledge, representing the full joint distribution, conditional independence relations, joint probability and conditional probability |
| 15 | Final review | review whole lecture contents and explain example solutions to work in class and home work |
授業外に行うべき学習活動
Read online teaching materials
テキスト
・エージェントアプロ−チ人工知能, Stuart Russell and Peter Norvig 著, 古川康一監訳、共立出版, ISBN 4-320-02878-3 (Japanese version). Online teaching materials and demonstration systems
参考書
・Artificial Intelligence - A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall, ISBN 0-13-103805-2 (English version). ・Artificial Intelligence, 3rd Edition, Patrick Henry Winston, Addison Wesley, ISBN 0-201-53377-4.
成績評価基準
The evaluation is based on attendance, work in class, home work, and final examination.
前年度の授業改善アンケートからの気づき
Introducing the contents in Japanese gradually