The 2nd European Summer School in Artificial Intelligence
ESSAI 2024 is the second edition of the annual summer school on AI held under the auspices of the European Association for Artificial Intelligence (EurAI). ESSAI 2024 will provide an interdisciplinary setting in which courses are offered in all areas of Artificial Intelligence and also from wider scientific, historical, and philosophical perspectives. ESSAI is a central meeting place for students and young researchers in Artificial Intelligence to discuss current research and share knowledge. Courses will consist of five 90-minute sessions, offered daily (Monday-Friday) in a single week, to allow students to develop in-depth knowledge of a topic.
The first edition of ESSAI was held in Ljubljana, Slovenia in between the 24th and 28th of July 2023 and was extremely successful, attracting over 500 participants. We look forward to a second edition of ESSAI that is at least as successful.
Important Dates
- 07 Feb 2024: Course Title submission deadline (mandatory)
- 14 Feb 2024: Final submission
- 06 Mar 2024: Notification
Topics and format
ESSAI aims to cover all subdisciplines of AI and the interactions between them. Each course will consist of five 90-minute lectures, offered daily (Monday-Friday) in a single week. While introductory courses will typically focus on one subarea of AI, advanced courses are encouraged to present a broader perspective on AI, and should be of interest beyond one specific area. Proposals for courses at ESSAI 2024 are invited in all areas of Artificial Intelligence, including but not limited to the following:
- Autonomous Agents and Multi-agent Systems (MAS)
- Causality and Causal Learning (CL)
- Ethical, Legal and Social Aspects of AI (ELS)
- Foundation Models (FM)
- Knowledge Representation and Reasoning (KR)
- Learning Theory (LT)
- Natural Language Processing (NLP)
- Neuro-Symbolic Learning and Reasoning (NSLR)
- Planning & Strategic Reasoning (PLAN)
- Reinforcement Learning (RL)
- Robotics (ROB)
- Safe, Explainable and Trustworthy AI (SET)
- Search & Optimization (SO)
- Supervised and Unsupervised Learning (ML)
- Vision (VIS)
Categories
Each proposal should fall under one of the following categories:
Introductory courses are intended to introduce a research field to students, young researchers, and other non-specialists, and to foster a sound understanding of its basic methods and techniques. Such courses should enable researchers from related disciplines to develop some comfort and competence in the topic considered. Introductory courses in a cross-disciplinary area may presuppose general knowledge of the related disciplines.
Advanced courses are targeted primarily to graduate students who wish to acquire a level of comfort and understanding in the current research of a field.