Multiagent systems combine multiple, autonomous, distributed computational entities such as AI agents, each having diverging interests or different information. This course aims to offer the comprehensive overview of this fast-growing field but also concentrate on foundational topics rather than surface applications, with thorough discussions of distributed optimization, cooperative and noncooperative games, multiagent learning, social choice, mechanism design, auctions, coalitional game, and logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. And so this is wrong course for the students interested in a practical guide to building this or that sort of software. Many of the ideas apply to-and indeed are often taken from-inquiries about the behavior of human individuals and institutions.
|Song Chong||TBA||IT Center (N1)-email@example.com|
|Seyeon Kim||TBA||IT Center (N1)-firstname.lastname@example.org|