Course title
Intelligent Information Systems Exercise 1

Course content
In the field of artificial intelligence, research in neural computing, distributed artificial intelligence, and complex system has been active since around 1980. What is common about these research subjects is their targeting of large-scale and difficult problems in the natural science and social science fields, and their algorithm design ideology to solve the problems of control and learning for the overall system by modeling systems as groups of simple information processing units such as neuro elements and agents. In the backdrop of such research, research methods from fields other than conventional information science have played a large role. They include improvements in computer performance enabling large-scale calculations for large-scale simulations and machine learning, information processing models in the human brain, the evolutionary process of organisms, and technical methods for handling many-body systems in statistical physics.

The following research will be conducted from the aforementioned perspective.

1. Basic research in multi-agent systems
Agent modeling, agent learning through reinforcement learning, emergence and learning of coordinated behavior
2. Applied research in multi-agent systems
Pursuit problem, soccer game, navigation of mobile robots, path planning, search in optimization problems
3. Basic theoretical research in reinforcement learning
Reinforcement learning method in the non-Markovian process
4. Basic research in natural language processing
Semantic analysis of natural language sentence, application and acquirement of language knowledge, statistical natural language processing
5. Applied research in natural language processing
Interactive user interface, information search and integration on the Web, application in education and daily living
Goals and objectives
  1. Understand basic theories and techniques of machine learning such as neurocomputing and reinforcement learning
  2. Understand basic concetps and structures of multiagent systems and make a simple simulation system
  3. Understand basic theories and skills of natural language processing and apply those skills to simple examples
Japanese(English accepted)
Class schedule
Apply new technologies and theories in this field to research themes. That will develop student's ablities of problem finding and resolution. Challenging to basic and theoretical research topis is more desirable.
Evaluation method and criteria
Students will be evaluated based on progress reports (more than 3 seminars/week) (50%), academic society presentations (10%), and writing of master's theses (40%).
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Textbooks and reference materials
Will be specified, if necessary.
Probability theory, linear algebra, knowledge on partial differential, and discrete mathematics
Office hours and How to contact professors for questions
  • T. Sugimoto: 12:30-13:20, Thursday
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates a basic problem-solving skills
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
Most classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
Education related SDGs:the Sustainable Development Goals
Last modified : Wed Mar 27 04:05:16 JST 2024