  High-level reasoning can be defined as the capability to generalize over
knowledge acquired via experience, and to exhibit robust behavior in novel
situations. Such form of reasoning is a basic skill in humans, who seamlessly
use it in a broad spectrum of tasks, from language communication to decision
making in complex situations. When it manifests itself in understanding and
manipulating the everyday world of objects and their interactions, we talk
about common sense or commonsense reasoning. State-of-the-art AI systems don't
possess such capability: for instance, Large Language Models have recently
become popular by demonstrating remarkable fluency in conversing with humans,
but they still make trivial mistakes when probed for commonsense competence; on
a different level, performance degradation outside training data prevents
self-driving vehicles to safely adapt to unseen scenarios, a serious and
unsolved problem that limits the adoption of such technology. In this paper we
propose to enable high-level reasoning in AI systems by integrating cognitive
architectures with external neuro-symbolic components. We illustrate a hybrid
framework centered on ACT-R and we discuss the role of generative models in
recent and future applications.
