This tutorial explains AI agent architectures, compares reactive, deliberative, hybrid, neural‑symbolic and cognitive designs, shows their trade‑offs, provides Python code examples for each, and links these patterns to LangGraph design templates for building scalable intelligent systems.
This article reviews recent advances in knowledge graph reasoning, introduces the RNNLogic framework that jointly learns a rule‑generating LSTM and a stochastic logic programming predictor, and demonstrates its competitive performance and interpretability on benchmark datasets while outlining future neural‑symbolic directions.
This talk reviews recent progress in neural‑symbolic learning and multimodal knowledge discovery, highlighting examples such as GPT‑3 reasoning failures, the need for symbolic knowledge, historical developments, various integration methods, challenges in multimodal knowledge graphs, and future research directions.
In an interview at the 2018 AI Pioneer Conference, Dr. Lv Zhengdong discusses his background, the development of neural‑symbolic AI at DeepCuriosity, its applications in law, public security and finance, challenges in NLP, and his perspective on the future trajectory of artificial intelligence.
Dr. Lv Zhengdong discusses the challenges of true language understanding, the integration of symbolic reasoning with neural networks, recent advances in neural‑symbolic models, and the practical prospects of NLP in domains such as law and finance, emphasizing the need for hybrid approaches.
