XP33DID: Distributed Artificial Intelligence

The course introduces Ph.D. students to distributed problem solving and multi-agent systems. The detailed content of the course is adjusted according to the background knowledge of registered students.

Topics

The topics discussed during the course are:

Distributed problem solving. Difference of distributed problem solving approach from multi-agent systems.
Agent and its properties. Types of agents and agent architecture.
Cooperation. Coordination. Communication. Communication strategies, message passing. Various AI approaches, case studies.
Types of agent behavior. Negotiation. Organizational structuring. Partial global planning.
Blackboard systems. Client-server systems. Peer-to-peer systems.
Implementation aspects of distributed knowledge-based systems.
Learning in multiagent systems. Meta-agent.
Agents acquitance models, social knowledge, reflectivity in MAS. Coalition formation, team work.
Formal models of agent architecture. Case studies.

Lectures

In winter semester 2021/22, the course will be organized via MS Teams, reusing the team for last semester 2020/21. The location and time of lectures will be discussed there, students will find the study materials there too.

Seminars

The students find a topic related to their own research, which has a link to the subject and uses its methods. The students consult the topic with the teacher. They find the most relevant journal articles and/or conference papers and consult them with the teacher. Then they study the selected texts individually. Afterwards they prepare presentations and the content is discussed in the whole group. The presentation is a condition for passing the seminars.

Exam and its evaluation

The exam is oral. It is based on the topics discussed during the course.

Requirements

To pass this course, the students have to

take part in lectures and seminars (2 absences are allowed),
prepare a presentation of the chosen article, and
pass the exam.

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