Today, artificial intelligence is mostly aboutartificial neural networksanddeep learning. But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as “classical AI,”“rule-based AI,” and “good ...
Training deep convolutional neural networks to play Go. In 32nd Int. Conf. on Machine Learning 1766–1774 (PMLR, 2015); http://proceedings.mlr.press/v37/clark15.html Winands, M. Neural networks for video game AI. In Artificial and Computational Intelligence in Games: Integration (Dagstuhl ...
Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural...
Neural Bidirectional Convergence: A Method for Concept Learning in Neural Networks and Symbolic AIWeir, M KPolhill, GWeir, M. K., & Polhill, J. G. (1996). Neural bidirectional convergence: A method for concept learning in neural networks and symbolic AI. In J Mira-Mira & R. ...
X Zhang,VS Sheng 摘要: Explainability is an essential reason limiting the application of neural networks in many vital fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging the transparency of symbolic learning, the results are less evident than imagined. This ...
xlang-ai/xlang-paper-reading Star344 Paper collection on building and evaluating language model agents via executable language grounding agentreinforcement-learningcode-generationtool-useneural-symboliclarge-language-modelscomplex-reasoninglanguage-agentllm-roboticsweb-grounding ...
Symbolic Artificial Intelligence (AI) is a subfield of AI that focuses on the processing and manipulation of symbols or concepts, rather than numerical data. The goal of Symbolic AI is to build intelligent systems that can reason and think like humans by representing and manipulating knowledge and...
Symbolic AI algorithms have played an important role in AI's history, but they face challenges in learning on their own. After IBM Watson used symbolic reasoning to beat Brad Rutter and Ken Jennings at Jeopardy in 2011, the technology has been eclipsed by neural networks trained by...
当前人工智能 (AI) 和机器学习的进步对研究界和行业产生了前所未有的影响。尽管如此,有影响力的思想家还是对人工智能的信任、安全、可解释性和问责制提出了担忧。许多人认为需要将有根据的知识表示和推理与深度学习相结合并实现合理的可解释性。多年来,神经符号计算一直是一个活跃的研究领域,旨在通过为神经模型提供...
A COMPREHENSIVE UNDERSTANDING OF NEUROSYMBOLIC AI: BRIDGING THE GAP BETWEEN NEURAL NETWORKS AND SYMBOLIC REASONINGSaini, Kritika PalGill, ShaliniYukubu, SamailaOORJA - International Journal of Management & IT