Thinking fast and automatic vs. slow and deliberate (respectively System I and II) is a popular analogy when comparing data-driven learning to the good old-fashion symbolic reasoning approaches. Underlying this analogy lies the different capabilities of both systems, or lack thereof. While data-driven learning (System I) has striking performance advantages over symbolic reasoning (System II), it lacks abilities such as abstraction, comprehensibility and contextual awareness. Symbolic reasoning, on the other hand, tackles those issues but tends to lag behind data-driven learning when it comes to speedy, efficient and automated decision-making. In the current state of matters to combat issues on both sides, there is an increasing consensus among the machine learning and artificial intelligence communities to draw out the best of both worlds and unify data-driven approaches with rule-based, symbolic, logical and commonsense reasoning. This workshop aims to discuss emerging advances and challenges on this topic, in particular at the intersection of data-driven paradigms and knowledge and logical reasoning. We focus on both directions of this intersection:
Knowledge and Logical Reasoning for Data-driven Learning: In this direction, we will investigate the role of rule-based, knowledge and logical reasoning to enable more deliberate and trustworthy data-driven learning.
Data-driven Learning for Knowledge and Logical Reasoning: In this reverse direction, we will explore the capabilities of data-driven approaches to derive knowledge, logical and commonsense reasoning from data.