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- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Nitta Katsumi Shinoda Koichi Inoue Katsumi
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon3-4(W631,G115) Thr3-4(W631, G115)
- Group
- -
- Course number
- ART.T548
- Credits
- 2
- Academic year
- 2017
- Offered quarter
- 2Q
- Syllabus updated
- 2017/3/17
- Lecture notes updated
- -
- Language used
- English
- 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.
Keywords
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 |
Textbook(s)
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.
