Neurο-symbolic answer set programming for human activity recognition in videos
View/
Abstract
Machine / Deep Learning and Machine Reasoning are considered two different
subfields of Artificial Intelligence. With machine learning methods we can build
models with low level perceptual capabilities and with logic based methods we
can extract information and perform reasoning at a higher level. Combining
neural learning methods with logic-based techniques could help create systems
that are able to perceive their environment and infer the data given as input. In
this thesis, we will focus on neurosymbolic computation, where the combination
of deep learning and reasoning is achieved through an existing framework called
NeurAsp. We will go through simple examples that demonstrate NeurAsp’s ca-
pabilities and show how it works and integrates internally with traditional deep
learning methods. The main goal of this thesis is to apply this method to the
task of detecting human activity in videos with the usage of Complex Event
Recognition (CER) techniques. Finally, we will show the benefits of integrating
logic-based techniques with neural methods by presenting three different exper-
imental setups in which we compare the performances of pure traditional deep
learning methods and those proposed by the NeurAsp framework.