WSN23601 Advanced Complex Systems Science

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program

Lecture Code WSN23601 Subject Classification Specialized Education

Subject Name 複雑システム科学特論

Subject Name
（Katakana）フクザツシステムカガクトクロン

Subject Name in
English Advanced Complex Systems Science

Instructor OGURA MASAKI

Instructor
(Katakana) オグラ　マサキ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Thur3-4,Fri3-4

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

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

Course Level 6 : Graduate Advanced

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students Students in the Information Science Program

Keywords Complex Systems

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)

Class Objectives
/Class Outline This course aims to develop students' ability to critically read, structure, and comparatively analyze academic literature across control theory, robotics, and information science, using topics such as cyclic pursuit; a representative case of swarm and distributed control. Through structured exercises in reading, classifying, and re-organizing the literature, students will produce foundational materials (classification axes, comparison tables, genealogy maps, open problem extraction) toward a future survey paper. Students will progressively engage in deep reading, comparison, visual structuring, and integration of scholarly resources.

Class Schedule Week 1: Guidance, course objectives, overview of cyclic pursuit
Week 2: What is a survey paper structure and classification axis? (Analytical vs Descriptive)
Week 3: Deep reading of core papers I – extracting goals and assumptions
Week 4: Deep reading II – comparison and axis mapping
Week 5: Drafting and discussing tentative classification maps
Week 7–10: Literature deep dives and classification presentations
Week 11: Creating classification maps, comparison tables, and genealogy diagrams
Week 12: Extracting cross-domain trends and unresolved problems
Week 13: Integrating and editing outcomes
Week 14–15: Final presentations, peer review, and reflective discussions

Text/Reference
Books,etc. Materials will be distributed online.

PC or AV used in
Class,etc.

(More Details)

Learning techniques to be incorporated Discussions, Paired Reading, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning

Suggestions on
Preparation and
Review rasp the overall structure in Week 1 and relate it to your interests. In Week 2, preview examples of classification axes and develop a structural perspective. For Weeks 3–4, deeply read the core papers and identify their assumptions and objectives. In Week 5, prepare a tentative classification idea and compare with peers. In Week 6, lightly research your assigned domain. For Weeks 7–10, thoroughly read your assigned papers and summarize them with comparative points. In Week 11, consolidate draft materials such as tables. Week 12 focuses on spotting unresolved issues and cross-cutting insights. In Week 13, consider how to integrate results, and in Weeks 14–15, focus on polishing presentations and reflecting on the overall process.

Requirements

Grading Method Overall evaluation based on exercises, reports, presentations, and discussion contributions

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message This course is not just about “reading” academic papers, but about cultivating the ability to “compare, structure, and rediscover” their meaning. Anyone with curiosity in control theory, robotics, or information science is welcome—especially those eager to explore research even if they feel unsure about reading English papers. We value willingness to think over prior expertise. Let’s chart a new map of this field together.

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	2026Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program
Lecture Code	WSN23601	Subject Classification	Specialized Education
Subject Name	複雑システム科学特論
Subject Name （Katakana）	フクザツシステムカガクトクロン
Subject Name in English	Advanced Complex Systems Science
Instructor	OGURA MASAKI
Instructor (Katakana)	オグラ　マサキ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Thur3-4,Fri3-4
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face

Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	6 : Graduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students	Students in the Information Science Program
Keywords	Complex Systems
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)
Class Objectives /Class Outline	This course aims to develop students' ability to critically read, structure, and comparatively analyze academic literature across control theory, robotics, and information science, using topics such as cyclic pursuit; a representative case of swarm and distributed control. Through structured exercises in reading, classifying, and re-organizing the literature, students will produce foundational materials (classification axes, comparison tables, genealogy maps, open problem extraction) toward a future survey paper. Students will progressively engage in deep reading, comparison, visual structuring, and integration of scholarly resources.
Class Schedule	Week 1: Guidance, course objectives, overview of cyclic pursuit Week 2: What is a survey paper structure and classification axis? (Analytical vs Descriptive) Week 3: Deep reading of core papers I – extracting goals and assumptions Week 4: Deep reading II – comparison and axis mapping Week 5: Drafting and discussing tentative classification maps Week 7–10: Literature deep dives and classification presentations Week 11: Creating classification maps, comparison tables, and genealogy diagrams Week 12: Extracting cross-domain trends and unresolved problems Week 13: Integrating and editing outcomes Week 14–15: Final presentations, peer review, and reflective discussions
Text/Reference Books,etc.	Materials will be distributed online.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated	Discussions, Paired Reading, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning
Suggestions on Preparation and Review	rasp the overall structure in Week 1 and relate it to your interests. In Week 2, preview examples of classification axes and develop a structural perspective. For Weeks 3–4, deeply read the core papers and identify their assumptions and objectives. In Week 5, prepare a tentative classification idea and compare with peers. In Week 6, lightly research your assigned domain. For Weeks 7–10, thoroughly read your assigned papers and summarize them with comparative points. In Week 11, consolidate draft materials such as tables. Week 12 focuses on spotting unresolved issues and cross-cutting insights. In Week 13, consider how to integrate results, and in Weeks 14–15, focus on polishing presentations and reflecting on the overall process.
Requirements
Grading Method	Overall evaluation based on exercises, reports, presentations, and discussion contributions
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	This course is not just about “reading” academic papers, but about cultivating the ability to “compare, structure, and rediscover” their meaning. Anyone with curiosity in control theory, robotics, or information science is welcome—especially those eager to explore research even if they feel unsure about reading English papers. We value willingness to think over prior expertise. Let’s chart a new map of this field together.
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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