Learning in Collaborative Multi-Agent Systems
Learning in Collaborative Multi-Agent Systems
Lecturer: Dr.-Ing. Nugroho Fredivianus
Hours per week: 2 for lecture + 2 for exercise
Language: English
Schedules:
Lectures: every Tuesday 14:00 - 16:00, Room 1332, Start: 25.10.2016
Exercises: every Thursday 08:00 - 10:00, Room -1319, Start: 27.10.2016
Examination: oral exam (25 min.) or written exam (120 min.)
Schedule:tbd
Room: tbd
Assigned for: Informatik, Elektrotechnik, Wirtschafts-Ing., Mechatronik
Course of study: Master (M.Sc.)
Number of participants: max 20 students
Goal: The understanding of collaborative distributed systems esp. Multi-Agent Systems (MAS), whose intelligence obtained after performing a specific Machine Learning method, e.g., decentralized market control, a team of robotic soccer.
Content:
- Agent model and Self-X properties
- Collaboration and competition in Multi-Agent Systems
- Nature-inspired algorithms
- Machine learning, esp. reinforcement learning
- Real application examples: robotic soccer team and more
Additional information: the exercises are mainly based on programming in NetLogo (free Multi-Agent Simulator), downloadable at http://ccl.northwestern.edu/netlogo/index.shtml
Literature: the following literature will be extended during the lectures:
- Gerhard Weiss (ed.): Multiagent Systems – A Modern Approach to Distributed Artificial Intelligence
- Mohri Mehryar et al: Foundations of Machine Learning
- Eric Bonabeau et al: Swarm Intelligence – From Natural to Artificial Systems
- Sven Brueckner et al (ed.): Engineering Self-Organising Systems: Methodologies and Applications
- Weiming Shen et al: Multi-Agent Systems for concurrent Intelligent Design and Manufacturing
Attention: all lecture slides will be available at the Moodle site.
The link to the course/lecture in HIS-LSF can be find here.