A Survey on Neural-symbolic Learning Systems
In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they have been found to lack effective reasoning and cognitive ability. On the other hand, symbolic systems exhibit exceptional cognitive intelligence but suffer from poor learning capabilities when compared to neural systems. Recognizing the advantages and disadvantages of both methodologies, an ideal solution emerges: combining neural systems and symbolic systems to create neural-symbolic learning systems that possess powerful perception and cognition. The purpose of this paper is to survey the advancements in neural-symbolic learning systems from four distinct perspectives: challenges, methods, applications, and future directions. By doing so, this research aims to propel this emerging field forward, offering researchers a comprehensive and holistic overview. This overview will not only highlight the current state-of-the-art but also identify promising avenues for future research.
Code (0)
등록된 구현이 없습니다.
Tasks
SurveySimilar Papers 제목 키워드 기반
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence. Neurosymbolic artificial intelligence (…
Logical ReasoningSurveyDimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificia…
SurveyNeuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era
The integration of symbolic computing with neural networks has intrigued researchers since the first theorizations of Artificial intelligence (AI). The ability of Neuro-Symbolic (NeSy) methods to infer or exploit behavio…
A Survey on Symbolic Knowledge Distillation of Large Language Models
This survey paper delves into the emerging and critical area of symbolic knowledge distillation in Large Language Models (LLMs). As LLMs like Generative Pre-trained Transformer-3 (GPT-3) and Bidirectional Encoder Represe…
Knowledge DistillationSurveyKnowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey
This comprehensive survey examines the integration of knowledge-based approaches into autonomous driving systems, with a focus on trajectory prediction and planning. We systematically review methodologies for incorporati…
Autonomous DrivingFormal LogicSurveyTrajectory Prediction