Hyungil Ahn, Rosalind W. Picard

Abstract

In this paper we present a new computational framework of affective-cognitive learning and decision making for affective agents, inspired by human learning and recent neuroscience and psychology. In the proposed framework ‘internal reward from cognition and emotion’ and ‘external reward from the external world’ serve as motivation in learning and decision making. We construct this model, integrating affect and cognition, with the aim of enabling machines to make smarter and more human-like decisions for better human-machine interactions.