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2019　Modeling of Discrete Systems

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Konagaya Akihiko  Deguchi Hiroshi  Ono Isao 
Class Format
Lecture / Exercise     
Media-enhanced courses
Day/Period(Room No.)
Mon1-2(J233)  Thr1-2(J233)  
Group
-
Course number
ART.T455
Credits
2
Academic year
2019
Offered quarter
2Q
Syllabus updated
2019/4/3
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, meta-heuristics and object-oriented modeling as well as Java programming. Both lectures and exercises will be done in the form of group learning with various background knowledge and skills. 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 through group learning.

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) Explain local search, simulated annealing, tabu search and stochastic multi point search.
5) Model discrete systems with the object oriented modeling techniques and implement the systems with Java.
6) Solve problems by appropriately communicating to each other in a group.

Keywords

Discrete systems, decision theory, game theory, metaheuristics, 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 Metaheuristics 1: local search and simulated annealing. Understand Metaheuristics, local search and simulated annealing.
Class 7 Metaheuristics 2: tabu search and stochastic multi point search. Understand tabu search and stochastic multi point search.
Class 8 Project assignment and group formation. Understand the project assignment and ice breaking with each other in a group.
Class 9 Object-oriented modeling. Understand object-oriented modeling.
Class 10 Java 1: types, classes, objects and methods. Understand types, classes, objects and methods in Java.
Class 11 Java 2: inheritance, control structures, exception and class library. Understand inheritance, control structures, exception and class library.
Class 12 Designing discrete systems. Design a discrete system with object-oriented modeling technique.
Class 13 Implementing and testing discrete systems. Implement a discrete systems with Java and test it.
Class 14 Simulation Conduct simulations of the discrete system and summarize the results of the project assignment.
Class 15 Presentation Explain the results of the project assignment.

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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