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Sun, C., Qian, H., & Miao, C. (2022). From psychological curiosity to artificial curiosity: Curiosity-driven learning in artificial intelligence tasks. arXiv preprint arXiv:2201.08300. 
Resource type: Journal Article
BibTeX citation key: Sun2022
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Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, General, Mathematics, Neuroscience
Subcategories: AI transfer learning, Decision making, Deep learning, Forecasting, Human learning, Machine learning
Creators: Miao, Qian, Sun
Collection: arXiv preprint arXiv:2201.08300
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning as inspired by human cognitive development; meanwhile, it can bridge the existing gap between AI research and practical application scenarios, such as overfitting, poor generalization, limited training samples, high computational cost, etc. As a result, curiosity-driven learning (CDL) has become increasingly popular, where agents are self-motivated to learn novel knowledge. In this paper, we first present a comprehensive review on the psychological study of curiosity and summarize a unified framework for quantifying curiosity as well as its arousal mechanism. Based on the psychological principle, we further survey the literature of existing CDL methods in the fields of Reinforcement Learning, Recommendation, and Classification, where both advantages and disadvantages as well as future work are discussed. As a result, this work provides fruitful insights for future CDL research and yield possible directions for further improvement.
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