  In recent years, data-intensive AI, particularly the domain of natural
language processing and understanding, has seen significant progress driven by
the advent of large datasets and deep neural networks that have sidelined more
classic AI approaches to the field. These systems can apparently demonstrate
sophisticated linguistic understanding or generation capabilities, but often
fail to transfer their skills to situations they have not encountered before.
We argue that computational situated grounding provides a solution to some of
these learning challenges by creating situational representations that both
serve as a formal model of the salient phenomena, and contain rich amounts of
exploitable, task-appropriate data for training new, flexible computational
models. Our model reincorporates some ideas of classic AI into a framework of
neurosymbolic intelligence, using multimodal contextual modeling of interactive
situations, events, and object properties. We discuss how situated grounding
provides diverse data and multiple levels of modeling for a variety of AI
learning challenges, including learning how to interact with object
affordances, learning semantics for novel structures and configurations, and
transferring such learned knowledge to new objects and situations.
