Distributed systems

Lecturer: Vrabič Rok

Syllabus outline:

• Markov decision process
• Value and policy iteration
• Q-learning
• Introduction to game theory
• Normal form games
• Extensive form games
• Network theory
• Random networks
• Network analysis
• Distributed system modelling
• Information-communication infrastructure for distributed systems
• Analysis of case studies

Objectives and competences:

The main objective of the course is to introduce the theory and practice of distributed systems, their modelling, and applications relevant for manufacturing systems. The course deals with decision making of a single agent through Markov decision process theory, multi-agent decision making through game theory, and multi-agent system modelling using network theory and analysis. The emphasis is given to information and communication structure for modern and future manufacturing systems. Several case studies are presented.

Intended learning outcomes:

Knowledge and understanding:
Understanding decision-making of a single agent, understanding decision-making, when an agent is faced with an environment that includes other agents, modelling and analysis of distributed systems using network theory, knowledge and understanding of modern and future information-communication structures.