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

Autor:	Geh, Renato Lui, Gonçalves, Jonas, Silveira, Igor Cataneo, Mauá, Denis Deratani, Cozman, Fabio Gagliardi
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Logic in Computer Science Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 12 pages, 1 figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.02944 Zobrazit plný text záznamu View this record from Arxiv