2016 Intelligent Systems

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Terano Takao  Nitta Katsumi  Murofushi Toshiaki  Miyake Yoshihiro  Nakamura Kiyohiko 
Course component(s)
Lecture     
Day/Period(Room No.)
Tue3-4(S223,G311)  Fri3-4(S223,G311)  
Group
-
Course number
ART.T541
Credits
2
Academic year
2016
Offered quarter
1Q
Syllabus updated
2016/4/27
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

To students who have studied Introduction to Artificial Intelligence, this course teaches advanced topics about analysis and design of intelligent systems based on methodologies of artificial intelligence. Through these, this course aims to train students to understand advanced theories and techniques for developing practical systems.

Student learning outcomes

By the end of this course, students will be able to analyze and design 1) fuzzy set theory and decision-making process, 2) ontology, 3) brain informatics of learning mechanism, 4) co-creation system, and 5) agent based modeling by using artificial intelligence technology.

Keywords

Fuzzy Set Theory, Ontology, Braininformatics, Co-creation System, Agent Based Modeling

Competencies that will be developed

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

Class flow

At every class, topics specified in the course schedule are explained using practical examples. Also, students are given exercise problems related to the lecture given that day to solve. To prepare for a class, students should read the course schedule section and check what topics will be covered. Required learning should be completed outside of the classroom for preparation and review purposes.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Fuzzy Set Theory Understand the contents covered by the lecture.
Class 2 Non-additive probabilities and decision making Understand the contents covered by the lecture.
Class 3 Formal Concept Analysis Understand the contents covered by the lecture.
Class 4 Basis of Ontology Understanding basis of ontology
Class 5 Ontology in Web Understanding ontology in Web
Class 6 Predicate logic and Description logic Understanding relation between predicate logic and description logic
Class 7 Brain mechanism of reinforcement learning To be annouced in class
Class 8 Active learning To be annouced in class
Class 9 Neural mechanism for computation of information value To be annouced in class
Class 10 Intelligence as Co-creation System To be annouced in class
Class 11 Co-creation between Human and Artifacts To be annouced in class
Class 12 Communication and Co-creation To be annouced in class
Class 13 Agent Based Modeling and Simulation To be annouced in class
Class 14 Recommendation Systems To be annouced in class
Class 15 Business Applications of Artificial Intelligence To be annouced in class

Textbook(s)

none

Reference books, course materials, etc.

B. Ganter & R. Wille “Formal Concept Analysis — Mathematical Foundations” Springer, 1999: ISBN 978-3-540-62771-5

Assessment criteria and methods

Students are assessed on exercises (80%) and assignments (20%) in class.

Related courses

  • CSC.T261 : Logic in Computer Science
  • ZUS.I301 : Introduction to Artificial Intelligence
  • ART.T548 : Advanced Artificial Intelligence

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

Introduction to Artificial Intelligence

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