Neural Symbolic AI
Learning Algorithms via Neural Logic Networks
https://arxiv.org/abs/1904.01554
https://github.com/apayani?tab=repositories
Inductive Logic Programming via Differentiable Deep Neural Logic Networks
https://arxiv.org/pdf/1906.03523.pdf
https://github.com/apayani/ILP
Learning Explanatory Rules from Noisy Data
https://arxiv.org/pdf/1711.04574.pdf
https://github.com/ai-systems/DILP-Core Pytorch
Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning
https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12345/12022
Learn To Explain Efficiently via Neural Logic Inductive Learning
https://openreview.net/attachment?id=SJlh8CEYDB&name=original_pdf
https://openreview.net/forum?id=SJlh8CEYDB
Code: https://github.com/gblackout/NLIL
DeepProbLog: Neural Probabilistic Logic Programming
http://papers.nips.cc/paper/7632-deepproblog-neural-probabilistic-logic-programming.pdf
code: https://bitbucket.org/problog/deepproblog/src/master/
Neural Logic Machines
https://arxiv.org/abs/1904.11694
https://github.com/google/neural-logic-machines
Logical Rule Induction and Theory Learning Using Neural Theorem Proving
https://arxiv.org/pdf/1809.02193.pdf
Code? https://github.com/ACampero/diff-Theory-ILP
Neural Theorem Provers Do Not Learn Rules Without Exploration
https://arxiv.org/pdf/1906.06805.pdf
https://github.com/Michiel29/ntp-release
RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
https://arxiv.org/pdf/1909.07095.pdf
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
https://arxiv.org/pdf/1908.06177.pdf (надо дальше пройтись по ссылкам)
Bridging Machine Learning and Logical Reasoning by Abductive Learning
https://github.com/haldai/LogicalVision2
Using matrices to model symbolic relationships
http://papers.nips.cc/paper/3482-using-matrices-to-model-symbolic-relationship.pdf
Bridging Relational and Deep Learning
https://people.cs.kuleuven.be/~sebastijan.dumancic/RelationalDeepLearning/index.html
Learning Relational Representations with Auto-encoding Logic Programs
https://arxiv.org/pdf/1903.12577.pdf
Auto-encoding Logic Programs
https://sebdumancic.github.io/assets/pdf/2018/pubs/2018_DumancicGunsMeertBlockeel_NAMPI.pdf
Bias Specification Language
https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_73
Markov Logic Networks
https://homes.cs.washington.edu/~pedrod/papers/mlj05.pdf
code: https://alchemy.cs.washington.edu/
LEARNING INVARIANTS THROUGH SOFT UNIFICATION
https://arxiv.org/pdf/1909.07328.pdf
https://openreview.net/forum?id=r1xwA34KDB
https://github.com/nuric/softuni
DeepLogic: Towards End-to-End Differentiable Logical Reasoning
https://arxiv.org/pdf/1805.07433.pdf
https://github.com/nuric/deeplogic
DeepProbLog: Integrating Logic and Learning through Algebraic Model Counting
https://kr2ml.github.io/2019/papers/KR2ML_2019_paper_34.pdf
Learning Compositional Neural Programs with Recursive Tree Search and Planning
https://github.com/instadeepai/AlphaNPI
https://www.instadeep.com/research-article/towards-compositionality-in-deep-reinforcement-learning/
ELABORATION TOLERANCE John McCarthy
http://jmc.stanford.edu/articles/elaboration/elaboration.pdf
Using NLU in Context for Question Answering: Improving on Facebook's bAbI Tasks
https://arxiv.org/abs/1709.04558
Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization
https://arxiv.org/pdf/1906.00180.pdf
Library Learning for Neurally-Guided Bayesian Program Induction
Inductive Learning of Answer Set Programs from Noisy Examples
https://arxiv.org/pdf/1808.08441.pdf
Online Learning of Event Definitions
https://arxiv.org/pdf/1608.00100.pdf
Neural Logic Networks
https://arxiv.org/abs/1910.08629
Relational Neural Machines
https://arxiv.org/pdf/2002.02193.pdf
TensorLog: Deep Learning Meets Probabilistic Databases
https://arxiv.org/pdf/1707.05390.pdf
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
https://arxiv.org/pdf/1606.04422.pdf
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
https://arxiv.org/pdf/1702.08367.pdf
Neural-LP: https://github.com/fanyangxyz/Neural-LP
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP
https://link.springer.com/article/10.1007/s10994-018-5707-3
Surveys
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
https://arxiv.org/pdf/1905.06088.pdf
Program Synthesis
https://www.microsoft.com/en-us/research/wp-content/uploads/2017/10/program_synthesis_now.pdf
Courses:
Stanford Logic Programming
https://web.stanford.edu/~vinayc/logicprogramming/html/
http://logicprogramming.stanford.edu/chapters/index.html
AI Modern Approach
https://github.com/aimacode/aima-python
МГУ Математическая логика и логическое программирование
Software:
Problog
https://dtai.cs.kuleuven.be/problog/index.html
https://problog.readthedocs.io/en/latest/python.html