Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen g
The goal of neural-symbolic computation is to integrate the connectionist and symbolist paradigms. Prior methods learn the neural-symbolic models using reinforcement learning (RL) approaches, which ignore the error propagation in the symbolic reasoning module and thus converge slowly with sparse rewards...
The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop AI systems with more human-like reasoning capabilities by c
Neural Symbolic and Probabilistic Logic research explores integrating logic with neural networks. Key areas include surveys, logic-enhanced neural networks, mod...
论文(Zaremba & Sutskever, 2015) 设计了一些任务: ✅ 符号推理(Symbolic Reasoning):NTM 需要学会推理数学公式。✅ 强化学习导航(RL-based Navigation):NTM 作为智能体在网格世界中学习最优路径。✅ 程序执行(Learning to Execute):NTM 读取 Python 代码并预测输出。
machine-learningontologiesneural-symbolicneuro-symbolicneurosymbolic UpdatedJan 27, 2025 Python https://arxiv.org/abs/2312.10807 reinforcement-learningimitation-learningrobot-manipulationneural-symbolicfoundation-modelsvisual-language-modelslanguage-conditioned-learninglarge-languge-models ...
45、NeurosymbolicReinforcement Learningwith Formally Verified Exploration Greg Anderson (University of Texas at Austin) · Abhinav Verma (Rice University) · Isil Dillig (UT Austin) · Swarat Chaudhuri (The University of Texas at Austin) 46、Generalized Hindsight forReinforcement Learning ...
The best solvers today rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task, or whether an entirely different class of models are required. First, we adapt the DreamCoder neurosymbolic reasoning...
(head, relation, and tail) triplets. Since KGs are naturally discrete symbolic representations that can support explicit inferences and also can berepresented into the continuous vector space by deep learning models such as the state-of-the-art TransE (Bordes et al., 2013) to support implicit ...
nullNew–Generation Symbolic Model Checkers for Verifying Asynchronous Systems Learning and coding in biological neural networks - Harvard Understanding organizations as learning systems Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method NEURAL MACHINE TRANSLA...