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2021　Modeling of Discrete Systems

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Ono Isao  Yamamura Masayuki 
Class Format
Lecture / Exercise     
Media-enhanced courses
Day/Period(Room No.)
Mon1-2()  Thr1-2()  
Group
-
Course number
ART.T455
Credits
2
Academic year
2021
Offered quarter
2Q
Syllabus updated
2021/6/11
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

This course focuses on the modeling of discrete systems. There are many systems that we can model by regarding them as discrete systems in the fields of engineering, natural science and social science. Topics include discrete system modeling, decision theory, game theory, and object-oriented modeling as well as Java programming. This course enables students to understand and acquire the fundamentals of discrete systems modeling.
The aims of this course are 1) to enable students to acquire knowledge necessary for the design, implementation and analyses of discrete system models through numerical simulation, and 2) to improve their skills of problem solving, communication and presentation.

Student learning outcomes

By the end of this course, students will be able to:
1) Explain what the discrete system is, what isomorphism is, and examples of discrete systems.
2) Explain decision making principle, decision making under uncertainty, value of information, and Bayesian decision making.
3) Explain non-cooperative game, Nash equilibrium, game in extensive form, Repeated game, Evolutionary Game, and ESS：evolutionarily stable strategy.
4) Model discrete systems with the object oriented modeling techniques and implement the systems with Java.
5) Solve problems and explain the process of solving it.

Keywords

Discrete systems, decision theory, game theory, object-oriented modeling, Java

Competencies that will be developed

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

Class flow

At the end of each class, homework will be assigned. The aim of homework is to facilitate students to understand discrete system modeling in practice. At the beginning of each class, the solutions of the homework will be reviewed if necessary. Students must study the topics in the course materials in advance of the lectures.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Discrete systems, isomorphism and examples of discrete systems. Understand discrete systems, isomorphism and examples of discrete systems.
Class 2 What is decision making?, Decision making principle, Decision making under uncertainty. Understand decision making principle, decision making under uncertainty.
Class 3 Value of information, Bayesian decision making Understand value of information, Bayesian decision making.
Class 4 What is game? Non-cooperative game, Nash equilibrium. Understand non-cooperative game, Nash equilibrium.
Class 5 Game in extensive form, Repeated game, Evolutionary Game, ESS：evolutionarily stable strategy. Understand game in extensive form, Repeated game, Evolutionary Game, ESS：evolutionarily stable strategy.
Class 6 Ecological Systems Understand Logistic Growth, Inclusive Fitness
Class 7 Introduction to the second part. Understande the project assignment of the second part.
Class 8 Object-oriented modeling. Understand object-oriented modeling.
Class 9 Java language. Understanding the Java language.
Class 10 Implementing an agent (1) Implementing an agent with Java.
Class 11 Implementing an agent (2) Implementing an agent with Java.
Class 12 Pre-competition Feedback on pre-competion and improving the agent.
Class 13 Competition Conducting competition and summarizing the results of the project assignment.
Class 14 Presentation Making presentation on a summary of the project assignment of the second part.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None required. All material used in class can be found on WEB site.

Reference books, course materials, etc.

Herbert Schildt: Java: A Beginner’s Guide, Sixth Edition, Oracle Press. ISBN: 0071809252.

Assessment criteria and methods

Students will be assessed on their understanding of models of discrete systems, decision theory, game theory, metaheuristics, object-oriented modeling and object-oriented programming with Java and their ability to apply them to modeling discrete systems. Students’ course scores are based on assignments in each class (40%) and the final assignment (60%).

Related courses

  • ART.T452 ： Modeling of Continuous Systems

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

No prerequisites.

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