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2017　Foundations of Artificial Intelligence (ICT)

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Academic unit or major
Undergraduate major in Information and Communications Engineering
Instructor(s)
Okumura Manabu  Nakayama Minoru 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(S422)  Thr7-8(S422)  
Group
-
Course number
ICT.H318
Credits
2
Academic year
2017
Offered quarter
4Q
Syllabus updated
2018/4/9
Lecture notes updated
-
Language used
Japanese
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Course description and aims

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.

Student learning outcomes

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 that will be developed

✔ 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/Required learning

  Course schedule Required learning
Class 1 Introduction to artificial intelligence To understand what natural language processing technologies are To understand what artificial intelligence 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 To discuss the future of language processing technologies and text processing technologies To discuss the future of artificial intelligence technologies

Textbook(s)

No textbook

Reference books, course materials, etc.

Course materials are provided during class.

Assessment criteria and methods

Examination: 70%, exercises and reports: 30%

Related courses

  • ICT.H217 ： Logic and Reasoning

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None required

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