dPASP: A Comprehensive Differentiable Probabilistic Answer Set
  Programming Environment For Neurosymbolic Learning and Reasoning
Renato Lui Geh Jonas Gonçalves Igor Cataneo Silveira Denis Deratani Mauá Fabio Gagliardi Cozman
We present dPASP, a novel declarative probabilistic logic programming
framework for differentiable neuro-symbolic reasoning. The framework allows for
the specification of discrete probabilistic models with neural predicates,
logic constraints and interval-valued probabilistic choices, thus supporting
models that combine low-level perception (images, texts, etc), common-sense
reasoning, and (vague) statistical knowledge. To support all such features, we
discuss the several semantics for probabilistic logic programs that can express
nondeterministic, contradictory, incomplete and/or statistical knowledge. We
also discuss how gradient-based learning can be performed with neural
predicates and probabilistic choices under selected semantics. We then describe
an implemented package that supports inference and learning in the language,
along with several example programs. The package requires minimal user
knowledge of deep learning system's inner workings, while allowing end-to-end
training of rather sophisticated models and loss functions.