d’Avila Garcez AS, Gori M, Lamb LC, Serafini L, Spranger M, Tran S (2019) Neural-symbolic computing: an effective methodology for principled integration of machine learning and reasoning. FLAP 6(4):611–632 MathSciNetGoogle Scholar De Raedt L, Kimmig A, Toivonen H (2007) Problog: a ...
Neuro-Symbolic Reinforcement Learning with First-Order Logic 带有一阶逻辑的神经符号强化学习 emnlp 2021 论文地址:aclanthology.org/2021.e Abstract 深度强化学习(RL)方法在收敛之前往往需要多次试验,并且没有提供训练后的策略的直接可解释性。为了能在RL中实现快速收敛并且提供策略的可解释性,本文提出了一种基于文...
These are later used to perform downstream symbolic reasoning but symbolic knowledge is still engineered. Taking inspiration from Embed2Sym, this paper introduces a novel method for scalable neuro-symbolic learning of first-order logic programs from raw data. The learned clusters are optimally labelled...
Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within ...
This chapter explores the integration of symbolic reasoning and machine learning within the field of Neuro-Symbolic AI, a significant advancement in artificial intelligence. We investigate various reasoning methods, including deductive, inductive, abductive, analogical, probabilistic, common sense, and combin...
The Deep Learning group advances the state-of-the-art in deep learning to achieve general intelligence. We develop algorithms, models, and systems in deep supervised and unsupervised learning, deep reinforcement learning, and neural-symbolic reasoning, and then pursue breakthroughs in computer vision,...
then do that." Symbolic AI is a subset of machine learning that uses a combination of logical and mathematical processing to reason, make decisions and transform data into a more useful format. Symbolic processes are also at the heart of use cases such as solving math problems, improving data...
Neuro-Symbolic AI is a burgeoning field that marries two distinct realms of artificial intelligence: neural networks, which form the core of deep learning, and symbolic AI, which encompasses logic-based and knowledge-based systems. This synergy is designed to capitalize on the strengths of each ...
Over the last decades, deep neural networks based-models became the dominant paradigm in machine learning. Further, the use of artificial neural networks in symbolic learning has been seen as increasingly relevant recently. To study the capabilities of neural networks in the symbolic AI domain, rese...
Yu, D., Yang, B., Liu, D., Wang, H., Pan, S.: A survey on neural-symbolic learning systems. Neural Networks: Off. J. Int. Neural Network Soc. 166, 105–126 (2023). https://doi.org/10.1016/j.neunet.2023.06.028 Hassabis, D., Kumaran, D., Summerfield, C., Botvinick, M....