Dagstuhl-Seminar — Generalization by People and Machines

This Dagstuhl Seminar provides a unique opportunity for discussing the discrepancy between human and AI generalization mechanisms and crafting a vision on how to align the two streams in a compelling and promising way that combines the strengths of both. To ensure an effective seminar, we aim to bring together cross-disciplinary perspectives across computer and cognitive science fields. Our participants will include experts in Interpretable Machine Learning, Neuro-Symbolic Reasoning, Explainable AI, Commonsense Reasoning, Case-based Reasoning, Analogy, Cognitive Science, and Human-Computer Interaction. Specifically, the seminar will focus on the following questions: How can cognitive mechanisms in people be used to inspire generalization in AI? What Machine Learning methods hold the promise to enable such reasoning mechanisms? What is the role of data and knowledge engineering for AI and human generalization? How can we design and model human-AI teams that can benefit from their complementary generalization capabilities? How can we evaluate generalization in humans and AI in a satisfactory manner?

Topics: Interpretable Machine Learning, Human-AI Collaboration, Cognitive Science, Neuro-Symbolic Reasoning, Explainability