2017 Advanced Artificial Intelligence

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Academic unit or major
Graduate major in Artificial Intelligence
Nitta Katsumi  Shinoda Koichi  Inoue Katsumi 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Mon3-4(W631,G115)  Thr3-4(W631, G115)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

Based on the basic knowledge gained through the course "Introduction to Artificial Intelligence", this course teaches advanced technologies of knowledge representation, inference mechanism and human interfaces.

The aim of this course is that students learn the theoretical basis and applied technologies to develop advanced intelligent systems. 

Student learning outcomes

By the end of this course, students will learn the following:
1. Deductive reasoning and logic programming
2. Advanced technologies of logic programming (Inductive reasoning, abductive reasoning, probabilistic reasoning and so on.)
3. Sequential analysis of signals.
4. Methods of document analysis
5. Method of interactive systems
6. Method of Human Interface.


knowledge representation, deductive reasoning, inductive reasoning, abductive reasoning, probabilstic reasoning, ontology,predicate logic, description logic, logic programming, probablistic model

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Every class consists of a lecture using the slides and the exercise. Students are required to download the materials of lecture and read them before the class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Basis of Artificiall Intelligence and its history Considering criteria of Artificial Intelligence and relation between AI and heart.
Class 2 Knowledge representation:  production system, frame, semantic network Understanding features of various knowledge representation
Class 3 Knowledge representation: ontology, semantic Web, description logic Understanding basis of ontology and ontology in Web
Class 4 Knowledge representation and inference: Axiomatic logic Understanding basis of Axiomatic logic
Class 5 Knowledge representation and inference: Predicate logic and logic programming Understatnding basis of Predicate logic
Class 6 Higher inference: Nonmonotonic inference, answer set programming Understanding basis of inference with exception
Class 7 Higher inference: Inference concerning state change and action, and planning Understanding inference about action of an agent
Class 8 Heigher inference: Update of belief and knowledge Understanding inference about knolwdge of a human snd an agent
Class 9 Higher inference: Abductive reasoning Understanding inference about hypothesis
Class 10 Higher inference: Indective logic programming Undestanding method of inductive logic programming
Class 11 Uncertain knowledge and inference: Bayes' rule Understanding Bayes' rule
Class 12 Uncertain knowledge and inference: Probablistic reasoning with temporal information Understanding probabilistic reasoning
Class 13 Application: Advanced topics of Artificial Intelligence (Meta level abduction and its application) Understanding advanced topics in artificial intelligence
Class 14 Application: Advanced topics of Artificial Intelligence (Interactive agent) Understanding advanced topics in artificial intelligence
Class 15 Application: Advanced topics of Artificial Intelligence (Apllication of deep learning) Understanding advanced topics in artificial intelligence


No textbook is set. Materials are distibuted before each lesson.

Reference books, course materials, etc.

Artificial Intelligence - A Modern Approach (Third Edition, Prejtice Hall), and so on.

Assessment criteria and methods

Excercises 50%
Term-end report 50%

Related courses

  • ZUS.I301 : Introduction to Artificial Intelligence

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

To obtain "Introduction to Artificial Intelligence" is desirable.

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