Agent-based systems can be seen as Artificial Intelligence with many interacting agents. Like in classical AI, these agents are goal-oriented and take decisions in a potentially unknown environment. Unlike classical AI, though, agents have a representation of the other agents, their potential decisions and their goal, a representation about the representations of other agents, and so forth. This induces complex patterns of strategic reasoning, both in competitive and cooperative interactions, which need to be formally modelled and analysed.
Agent-based systems is built upon three important methodologies: Logic, because of the focus on reasoning, Game-Theory, because of the focus on strategies, and Algorithms, because of the focus on artificial agents.
The course will be an investigation of the most important developments of AI in multi-agent contexts, touching upon themes such as opponent modelling, games with imperfect information, resource allocation, collective decision-making and electronic commerce applications.
Students will learn the basic methodologies for the design and the analysis of multi-agent systems, in competitive and cooperative interaction, both from the theoretical and the practical point of view.
Overview: definitions of agents, distributed AI and agents, intelligent agents, multi-agent systems, cooperation, agent application areas.
Reasoning: multi-agent epistemic logic, action logics, deliberation, BDI models.
Competitive models: strategies and equilibria, opponent modelling.
Cooperative models: bargaining and negotiation, resource allocation, inter-agent relationships.
Open Issues: development methodology, programming languages, standards.