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| 09:00-10:00 |
Learning with Logic: Neuro-Symbolic Methods for Grounded and Robust AI (abstract) 60 min
ABSTRACT. Modern AI systems have achieved remarkable capabilities, yet they continue to struggle with reasoning, robustness under uncertainty, semantic controllability, and reliable generalisation across diverse tasks and environments. Addressing these challenges requires methods that meaningfully integrate data-driven learning with explicit representations of structure, knowledge, and inference. In this talk, I present a neuro-symbolic perspective on these challenges, grounded in work on learning from answer sets and extended to reward learning, task representations, foundation models, and controlled generation. I explore how symbolic representations and logical semantics can be learned from raw and noisy data, integrated with neural architectures, and used to support interpretable inference and reasoning across a range of settings. The research challenges I address include learning symbolic abstractions from perception, learning robust task representations for reinforcement learning, integrating symbolic reasoning with foundation models, and controlling the semantic behaviour of generative models. The central question unifying this work is how to build AI systems that are not only accurate, but semantically grounded, robust, and capable of reasoning that generalises well beyond the tasks they were trained on. |