State-of-the-art AI has many successful and useful applications, but it is often also narrow in scope and demonstrates several limitations including the lack of deep understanding of information coming from data, the absence of common-sense reasoning, the difficulty in dealing with causality, and the inability to learn general concepts from few data. To solve a given problem, AI systems usually employ either machine learning or a logical reasoning approach. Each of these approaches has its strengths and weaknesses, but it is hoped that their combination that will bring about a new generation of advanced AI. It is indeed now recognized by the whole scientific AI community that end-to-end machine learning (or deep learning) approaches, although very successful in specific scenarios, cannot bring AI to the next level, and that we need to carefully and effectively combine both machine learning and reasoning techniques. The importance of this combination of methods can be seen in the many specific approaches to neuro-symbolic AI, as well as several workshops and other initiatives in this space.

In this workshop, we propose to bring together cutting-edge researchers in these areas to discuss the combination of machine learning and symbolic reasoning techniques at a more general and multi-disciplinary level by taking inspiration from cognitive theories of human decision making, such as the dual system theory of Daniel Kahneman.

The symposium aims to be a meeting point for all the research teams that are investigating or developing frameworks inspired by the two-system theory of D. Kahneman or other cognitive theories of human decision making. The main topics of the symposium are related to other existing initiatives that study how to combine machine learning approaches with AI reasoning techniques. All these initiatives are focused on specific aspects of the ML/KRR combination.

Organizing Committee