Learning theory
Related papers: 11
About
Learning theory is a broad field that formalizes how systems—biological or artificial—acquire knowledge, skills, and behaviors from experience or data. In its mathematical form, statistical learning theory provides the rigorous foundation for understanding when and why machine learning algorithms generalize from training examples to unseen situations, addressing questions of sample complexity, bias-variance tradeoffs, and model capacity. In robotics and AI, learning theory underpins the design of algorithms for supervised learning, reinforcement learning, and curiosity-driven exploration, helping engineers predict how well a robot will perform after training and how much data is needed to reach a target capability. Concepts like policy-gradient methods draw directly on learning-theoretic principles to explain motor skill acquisition in both machines and biological agents. Understanding learning theory matters because it moves AI and robotics development beyond empirical trial-and-error, offering principled guarantees about performance, safety, and efficiency. It also bridges cognitive science, education, and engineering, informing how robots can be designed to teach or learn alongside humans in real-world settings.
Top Researchers
Yuhai Wu
Institution: —
Vladimir Vapnik
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Pierre‐Yves Oudeyer
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Takeshi Furuhashi
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Nian‐Shing Chen
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Masayoshi Kanoh
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Vivien Lin
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Pierre-Yves Oudeyer
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Hassan Abuhassna
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Mohamad Azrien Mohamed Adnan
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Top Institutes
Top Cited Papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
Citations: 26957 • 1999
Exploring the synergy between instructional design models and learning theories: A systematic literature review
Hassan Abuhassna, Mohamad Azrien Mohamed Adnan, Fareed Awae
Citations: 40 • 2024
Computational Theories of Curiosity-Driven Learning
Pierre‐Yves Oudeyer
Citations: 32 • 2018
A Systematic Review on Robot-Assisted Special Education from the Activity Theory Perspective
Ahmed Tlili, Vivien Lin, Nian‐Shing Chen, Ronghuai Huang, Kinshuk Kinshuk
Citations: 31 • 2020
Computational Theories of Curiosity-Driven Learning
Pierre-Yves Oudeyer
Citations: 28 • 2018
A novel machine learning method based on generalized behavioral learning theory
Ömer Faruk Ertuğrul, M. Emin Tağluk
Citations: 26 • 2016
New concepts in team theory: mean field teams and reinforcement learning
Jalal Arabneydi
Citations: 23 • 2017
Learning Effects of Robots Teaching Based on Cognitive Apprenticeship Theory
Kenya Miyauchi, Felix Jimenez, Tomohiro Yoshikawa, Takeshi Furuhashi, Masayoshi Kanoh
Citations: 12 • 2020
Artificial Intelligence and Digital Technologies in the Future Education
Michael Gr. Voskoglou
Citations: 7 • 2023
STUDI KOMPARATIF : TEORI EDWARD LEE THORNDIKE DAN IMAM AL GHAZALI DALAM IMPLEMENTASINYA DI PEMBELAJARAN ANAK USIA DINI
Nur Kolis, Aisyah Fajar Putri Artini
Citations: 3 • 2022
Policy-Gradient Reinforcement Learning as a General Theory of Practice-Based Motor Skill Learning
Adrian M. Haith
Citations: 3 • 2025