dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning

• URL: http://arxiv.org/abs/2308.02944v1
• Date: Sat, 5 Aug 2023 19:36:58 GMT
• Title: dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning
• Authors: Renato Lui Geh, Jonas Gon\c{c}alves, Igor Cataneo Silveira, Denis Deratani Mau\'a, Fabio Gagliardi Cozman
• Abstract summary: We present dPASP, a novel declarative logic programming framework for differentiable neuro-symbolic reasoning. We discuss the several semantics for probabilistic logic programs that can express nondeterministic, contradictory, incomplete and/or statistical knowledge. We then describe an implemented package that supports inference and learning in the language, along with several example programs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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