ASP-Driven Visual Commonsense: A General Framework for Reasoning About Embodied Interaction in the Wild

We present a general framework for declaratively grounded visual commonsense (reasoning) about embodied interaction in naturalistic, in-the-wild settings relevant to a range of AI application domains. The core computational capabilities of the framework pertaining visual commonsense are driven by a robust neurosymbolic architecture primarily consisting of: (1) answer set programming based modelling of foundational aspects pertaining spatio-temporal dynamics, encompassing space, time, events, action, motion; (2) modularly integrated visual computing techniques constituting the neural substrate linking quantitative perceptual features serving as low-level counterparts to high-level semantic characterisations of (inter)active visual commonsense.

Practically, we also present a first open-release of the developed framework with the aim to promote independent extensions and real-world applied KRR. The release comprises: (a) demonstrated case-studies in domains such as autonomous driving, psychology and media studies; (b) systematic evaluation mechanisms for community benchmarking; and (c) supporting material such as tutorials and datasets.