KA241201 Semiotic AI

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA241201 Subject Classification Specialized Education

Subject Name 記号論的AI

Subject Name
（Katakana）キゴウロンテキエーアイ

Subject Name in
English Semiotic AI

Instructor HAYASHI YUSUKE

Instructor
(Katakana) ハヤシ　ユウスケ

Campus Higashi-Hiroshima Semester/Term 3rd-Year,  First Semester,  2Term

Days, Periods, and Classrooms (2T) Weds9-10,Thur9-10：ENG 219

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Lectures and Exercises

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 1 : Undergraduate Introductory

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic.

Data Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers.
（Abilities and Skills）
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Class Objectives
/Class Outline This course covers semiotic AI, which processes meaning through symbolic representation and logical reasoning.
Semiotic AI operates differently from probabilistic AI, such as generative AI, which has advanced significantly and utilizes probability-based models.
Semiotic AI was the dominant method prior to innovations in probabilistic AI technology.
However, recently, it has regained attention due to improved accuracy achieved by integrating symbolic and probabilistic approaches, such as Graph RAG.
Therefore, acquiring knowledge about symbolic AI is essential for understanding future developments in AI technology.

The primary goal of this course is to provide an overview of such symbolic AI.
To this end, students will first study insights from cognitive science, which explores human memory, understanding, and thought processes.
Then, students will learn about artificial intelligence, which aims to replicate human intelligence through information technology.

In the latter part of the course, students will interact with actual knowledge bases using knowledge graphs, create simple applications, and engage in peer review.

Class Schedule lesson1
Intoroduction
lesson2
Symbolic AI
lesson3
human memory and knowledge

lesson4
human memory and knowledge
lesson5
Problem solving
lesson6
Problem solving
lesson7
Production system
lesson8
Production system
lesson9
Knowledge Graph and RDF
lesson10
Knowledge Graph and RDF
lesson11
Knowledge Graph and RDF
lesson12
Knowledge Graph and RDF
lesson13
Knowledge Graph and RDF
lesson14
Knowledge Graph and RDF
lesson15
Presentation Session

At the end of the course, a test will be conducted to assess students' understanding of the course content and their individual reflections based on it.

Text/Reference
Books,etc. 以下の書籍をベースに講義を構成します．
興味があったら購入してください
谷口忠大  (著)　イラストで学ぶ人工知能概論改訂第2版 (KS情報科学専門書)
北原義典  (著)　イラストで学ぶ　認知科学

PC or AV used in
Class,etc. Handouts, Microsoft Teams, moodle

(More Details)

Learning techniques to be incorporated Discussions, Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning)

Suggestions on
Preparation and
Review For preparation and review, assignments will be given as a basic requirement, so please make sure to complete them thoroughly at the very least.

Requirements

Grading Method Overall evaluation will be based on the assignments given for preparation and review in each class session, as well as the final exam.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA241201	Subject Classification	Specialized Education
Subject Name	記号論的AI
Subject Name （Katakana）	キゴウロンテキエーアイ
Subject Name in English	Semiotic AI
Instructor	HAYASHI YUSUKE
Instructor (Katakana)	ハヤシ　ユウスケ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Weds9-10,Thur9-10：ENG 219
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lectures and Exercises
Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	1 : Undergraduate Introductory
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic. Data Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Knowledge and Understanding）・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. （Abilities and Skills）・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
Class Objectives /Class Outline	This course covers semiotic AI, which processes meaning through symbolic representation and logical reasoning. Semiotic AI operates differently from probabilistic AI, such as generative AI, which has advanced significantly and utilizes probability-based models. Semiotic AI was the dominant method prior to innovations in probabilistic AI technology. However, recently, it has regained attention due to improved accuracy achieved by integrating symbolic and probabilistic approaches, such as Graph RAG. Therefore, acquiring knowledge about symbolic AI is essential for understanding future developments in AI technology. The primary goal of this course is to provide an overview of such symbolic AI. To this end, students will first study insights from cognitive science, which explores human memory, understanding, and thought processes. Then, students will learn about artificial intelligence, which aims to replicate human intelligence through information technology. In the latter part of the course, students will interact with actual knowledge bases using knowledge graphs, create simple applications, and engage in peer review.
Class Schedule	lesson1 Intoroduction lesson2 Symbolic AI lesson3 human memory and knowledge lesson4 human memory and knowledge lesson5 Problem solving lesson6 Problem solving lesson7 Production system lesson8 Production system lesson9 Knowledge Graph and RDF lesson10 Knowledge Graph and RDF lesson11 Knowledge Graph and RDF lesson12 Knowledge Graph and RDF lesson13 Knowledge Graph and RDF lesson14 Knowledge Graph and RDF lesson15 Presentation Session At the end of the course, a test will be conducted to assess students' understanding of the course content and their individual reflections based on it.
Text/Reference Books,etc.	以下の書籍をベースに講義を構成します．興味があったら購入してください谷口忠大 (著)　イラストで学ぶ人工知能概論改訂第2版 (KS情報科学専門書) 北原義典 (著)　イラストで学ぶ　認知科学
PC or AV used in Class,etc.	Handouts, Microsoft Teams, moodle
(More Details)
Learning techniques to be incorporated	Discussions, Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning)
Suggestions on Preparation and Review	For preparation and review, assignments will be given as a basic requirement, so please make sure to complete them thoroughly at the very least.
Requirements
Grading Method	Overall evaluation will be based on the assignments given for preparation and review in each class session, as well as the final exam.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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