The Advanced Course on AI (ACAI) is a specialized course in Artificial Intelligence sponsored by EurAI.

The theme of the 2018 ACAI School is Statistical Relational Artificial Intelligence (StarAI).

StarAI is an emerging area that combines logical (or relational) AI and probabilistic (or statistical) AI.

Relational AI deals very effectively with complex domains involving many and even a varying number of entities connected by complex relationships, while statistical AI manages well the uncertainty that derives from incomplete and noisy descriptions of the domains. Both fields achieved significant successes over the last thirty years but evolved largely independently until about fifteen years ago, when the potential originating from their combination started to emerge. Statistical Relational Learning (SRL) was proposed for exploiting relational descriptions in statistical machine learning methods from the field of graphical models. Meanwhile, the scope of SRL was significantly advanced in StarAI to cover all forms of reasoning and models of AI. StarAI is nowadays an ample area encompassing many and diverse approaches.

The school includes courses on foundations of relational and statistical AI together with advanced courses on the new StarAI approaches and applications. The talks will provide theoretical background, practical examples and real applications where StarAI can play a role. Hands-on classes will be also organized where the main StarAI techniques will be applied to “small” examples.

Research topic/Special issue on Statistical Relational Artificial Intelligence in the Frontiers in Robotics and AI Journal.

The list of confirmed lecturers includes:

• Luc De Raedt: Probabilistic (Logic) Programming
• Paolo Frasconi: Kernels and Deep Networks for Structured Data
• Sebastian Riedel and Pasquale Minervini: Differentiable Program Interpreters
• Artur d’Avila Garcez: Neural-Symbolic Learning
• Marco Lippi: Applications of Statistical Relational Artificial Intelligence
• Sriraam Natarajan: Human Allied Statistical Relational AI
• Mathias Niepert and Alberto García Durán: Neural Link Prediction for Multi-Modal Knowledge Graphs
• Kristian Kersting: Lifted Statistical Machine Learning
• Fabrizio Riguzzi: Probabilistic Inductive Logic Programming
• Vaishak Belle: Effective Probabilistic Logical Reasoning and Learning in Continuous Domains

Co-located with:

• PLP 2018: 5th Workshop on Probabilistic Logic Programming, 1^st September 2018
• ILP 2018: Inductive Logic Programming Conference, 2^nd - 4^th September 2018