2026 (Current Year) Faculty Courses School of Engineering Undergraduate major in Information and Communications Engineering
Foundations of Artificial Intelligence (ICT)
- Academic unit or major
- Undergraduate major in Information and Communications Engineering
- Instructor(s)
- Takayuki Nishio
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- ICT.H318
- Number of credits
- 200
- Course offered
- 2026
- Offered quarter
- 4Q
- Syllabus updated
- Mar 5, 2026
- Language
- Japanese
Syllabus
Course overview and goals
As the introduction to Artificial Intelligence, we will study the basic idea and theories in AI. More specifically, we will learn the topics such as search, knowledge representation and reasoning, and planning.
Course description and aims
As the introduction to Artificial Intelligence, you can understand the basic idea and theories in AI, and can trace their algorithms.
Keywords
search, knowledge representation and reasoning, planning, semantic network, frame, default reasoning, production system, Bayesian network, frame problem
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
In the course, basics of each topic are given. Students are asked to do some exercises in the class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to artificial intelligence |
To understand what natural language processing technologies are |
| Class 2 | Search1: How we represent a problem, graph search |
To understand how the problems are represented and solved on computers |
| Class 3 | Search2: Heuristic search, A* algorithm |
To understand heuristic search |
| Class 4 | Search 3: Search in game playing |
To understand search algorithms in game playing |
| Class 5 | Knowledge representation 1: Semantic network |
To understand a method of knowledge representation, semantic network |
| Class 6 | Knowledge representation 2: Frame |
To understand a method of knowledge representation, frame |
| Class 7 | Knowledge representation 3: Production system |
To understand a method of knowledge representation, production rule |
| Class 8 | Reasoning 1: Default reasoning |
To understand a method of reasoning, default reasoning |
| Class 9 | Reasoning 2: Forward and backward reasoning |
To understand a method of reasoning, forward and backward reasoning |
| Class 10 | Reasoning 3: Probabilistic reasoning, Bayesian network |
To understand a method of reasoning, probabilistic reasoning |
| Class 11 | Problem solving: GPS (General problem solver) |
To understand the basic idea of General problem solver |
| Class 12 | Planning 1: Hierarchical planning |
To understand a method of planning, hierarchical planning |
| Class 13 | Planning 2: Frame problem |
To understand the Frame problem |
| Class 14 | Introduction to machine learning |
To understand the basic idea of machine learning and its basic algorithms |
| Class 15 | Applications |
自然言語処理技術，テキスト処理技術の今後について議論する |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
No textbook
Reference books, course materials, etc.
Course materials are provided during class.
Evaluation methods and criteria
Examination: 60%, exercises and reports: 40%
Related courses
- ICT.H217 ： Logic and Reasoning
Prerequisites
None required