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Affective Cognitive Learning and Decision Making

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cooloacca Hyungil Ahn

Affective Cognitive Framework for Machine Learning and Decision Making

Recent affective neuroscience and psychology indicate that human affect and emotional experience play a significant, and useful, role in human learning and decision making. Most machine learning and decision-making models, however, are based on old purely cognitive models. I aim to redress this problem, by developing new models that integrate affect with cognition. The first model is being built to address several very difficult problems in machine learning. My aim is to utilize affect-like mechanisms to fix several of these problems. I expect that an integrated affective-cognitive learning system should exhibit many improvements over the state of the art, ultimately enabling much smoother human-computer interaction and more intelligent human-machine systems.

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