Neural Logic Reinforcement Learning source: NLRL Framework 整个框架为: actor-critic, 并没有learning model, 而是learning action. Actor: αILP作为actor, 以atoms encoding的valuation vector作为输入. 输出是从result of valuation vector中转换得到的action probs. Critic:对state进行常规编码(如STACK任务中编码成...
The contradiction value indicates a level of contradiction for each of a plurality of logic rules implemented by the LNN structure. The method evaluates the action L with respect to safetyness based on the contradiction value. The method selectively performs the action responsive to an evaluation ...
为了验证所提出模型的学习效果,我们设计了几个实验,包括学习逻辑门和学习19态随机游走。 4.1 Learning a LogicGate. 活动瞬变的可靠门控是正常大脑功能的先决条件(Kremkow, Aertsen, & Kumar, 2010)。这要求信号路径根据信号的信息内容和接收器的处理需求动态改变(Vogels & Abbott, 2005, 2009)。这需要对信号传输...
Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, and Alexander Gray, "Neuro-Symbolic Reinforcement Learning with First-Order Logic", EMNLP 2021. Details and bibtex ...
A curated list of papers on Neural Symbolic and Probabilistic Logic. Papers are sorted by their uploaded dates in descending order. Each paper is with a descrip...
Neural Logic Reinforcement Learning. Jiang, Zhengyao & Luo, Shan. ICML 2019[code] Neural Logic Rule Layers. Reimann, Jan Niclas & Schwung, Andreas. arXiv:1907.00878 Neural Logic Networks. Shi, Shaoyun et al. arXiv:1910.08629[project] ...
论文《A Critical Review of Inductive Logic Programming Techniques for Explainable AI》主要探讨了归纳逻辑编程(ILP)在生成可解释性解释中的应用。ILP 是符号人工智能的一个子领域,通过其直观的逻辑驱动框架,利用归纳推理从示例和背景知识中生成可解释的一阶子句理论。尽管现代机器学习算法取得了进展,但其底层机制的不...
几篇论文实现代码:《Neural Arithmetic Logic Units》GitHub:http://t.cn/Eqzxo46 《Quasi-hyperbolic momentum and Adam for deep learning》(ICLR 2019) GitHub:http://t.cn/Eqzxo40 《DSFD: Dual Shot Face ...
Learning Structural Node Embeddings via Diffusion Wavelets Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec KDD 2018 Adversarial Network Embedding Quanyu Dai, Qiang Li, Jian Tang, Dan Wang AAAI 2018 GraphGAN: Graph Representation Learning with Generative Adversarial Nets ...
Katsunari SHIBATA, Yoichi OKABE, and Koji ITO: "Direct-Vision-Based Reinforcement Learning Using a Layered Neural Network -For the Whole Process from ... OK Ito - 計測自動制御学会論文集 被引量: 0发表: 2001年 Reinforcement structure/parameter learning for neural-network-basedfuzzy logic control...