Computer ScienceScientific Reports
A generic self-learning emotional framework for machines
A. Hernández-marcos and E. Ros
The paper proposes that emotions are distinct temporal patterns in key environmental values and introduces a fully self-learning emotional framework for AI. An artificial neural network trained on unlabeled agent experiences learned eight basic emotional patterns that mirror natural dynamics and were validated by human ratings. This research was conducted by Alberto Hernández-Marcos and Eduardo Ros.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses
Z. Zhou, Y. Shang, et al.
Computer Science
A deep learning framework for gender sensitive speech emotion recognition based on MFCC feature selection and SHAP analysis
Q. Hu, Y. Peng, et al.
Engineering and Technology
SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning
Y. Song, M. Zhang, et al.
Computer Science
A framework for the emergence and analysis of language in social learning agents
T. J. Wieczorek, T. Tchumatchenko, et al.
