Welcome to COMP 667—Multiagent Systems. Multiagent systems (MAS) can be defined as loosely coupled networks of problem solvers that interact to solve problems that are beyond the individual capabilities or knowledge of each problem solver. These problem solvers, often called agents, are autonomous and can be heterogeneous in nature.
Research and development in MAS is concerned with the study, behaviour, and construction of a collection of autonomous agents that interact with each other and their environments. The study of such systems goes beyond the study of individual intelligence in its consideration of problem solving with social components.
COMP 667 introduces students to the main topics in the theory and practice of MAS—currently one of the most important and rapidly expanding areas of computer science, having emerged from the study of distributed artificial intelligence. Multiagent systems have been used as an important means with which to address the development of large and complex information systems (IS) and decision support systems (DSS).
The course consists of seven units. Because game theory is a key tool to master within the field, Unit 1 introduces the student to the concepts in non-cooperative game theory, covering the normal form and the extensive form, which will pay the theoretical foundation for you to learn the key multiagent technologies.
Unit 2 discusses the origin of the multiagent systems paradigm, introduces a first definition of agents and multiagent systems, and hints at some applications and properties of intelligent agents.
Unit 3 covers an interesting and important topic, multiagent learning, in which agents make distributed decisions, called strategies, on how to use the shared resources. An agent learns from interactions with its environment, including those with the other agents, which strategies to play in order to improve its own long-term reward.
Unit 4 introduces social-choice theory, including voting methods and preference aggregation.
Unit 5 explains mechanism design, which looks at how such preferences can be aggregated by a central designer, even when agents are strategic.
Unit 6 examines the protocols for multiagent resource allocation (auctions).
Finally, Unit 7 of this course introduces coalitional game theory and its potential applications.
Since multiagent systems is a relatively new and ever-growing field, there is some uncertainty as to what the most important ideas are. This course concentrates on the theoretical aspects of multiagent systems and stays away from technological issues because they are evolving very fast. This course uses NetLogo as a tool to simulate emergent decentralized behaviours in multiagent systems and implement some problems in the assignments. NetLogo is a multiagent programmable modelling environment. You can download it free of charge from the NetLogo site.
Updated May 04 2018 by FST Course Production Staff