Whereas the first half of the tutorial concentrates on a single agent learning from environmental feedback, the latter half discusses particular issues that arise and techniques that have been used to address the corresponding problems when multiple agents learn in an environment. The ability to coordinate actions among multiple computational agents via dynamically made contracts is of key importance in multi-agent systems. These agents can represent real world parties, and they can have different preference structures. A key research goal is to design open distributed systems in a principled way that leads to globally desirable outcomes even though every participating agent only considers its own good and may act insincerely. The tutorial covers relevant results in AI, game theory, market mechanisms, voting, auctions, coalition formation, and contract nets.
Emphasis is given to results that have direct applications to computational multi-agent systems.
Effects of different computing limitations of the agents are discussed as an important characteristic that prevails specifically among computational agents. In addition to reviewing classic results, selected recent results from the multi-agent systems research community are discussed. Application examples will also be presented. It will introduce the foundations of BDI agents, describe systems built on these foundations, and examine business and industrial applications of BDI agents. It provides the theoretical background as well practical examples which have a significant impact on industrial application domains.