Artificial Emotion: A Survey of Theories and Debates on Realising Emotion in Artificial Intelligence

TL;DR

This survey explores the concept of Artificial Emotion (AE) in AI, discussing its potential benefits, current implementations, and ethical considerations, aiming to advance towards emotionally capable artificial general intelligence.

Contribution

It provides a comprehensive review of theories, current AE manifestations, and mechanisms for integrating emotion-like states into AI systems, highlighting the need for a clear framework.

Findings

AI systems show early signs of AE-like behaviors

Emotion-modulated architectures are emerging in machine learning

Ethical and safety issues are significant in AE development

Abstract

Affective Computing (AC) has enabled Artificial Intelligence (AI) systems to recognise, interpret, and respond to human emotions - a capability also known as Artificial Emotional Intelligence (AEI). It is increasingly seen as an important component of Artificial General Intelligence (AGI). We discuss whether in order to peruse this goal, AI benefits from moving beyond emotion recognition and synthesis to develop internal emotion-like states, which we term as Artificial Emotion (AE). This shift potentially allows AI to benefit from the paradigm of `inner emotions' in ways we - as humans - do. Although recent research shows early signs that AI systems may exhibit AE-like behaviours, a clear framework for how emotions can be realised in AI remains underexplored. In this paper, we discuss potential advantages of AE in AI, review current manifestations of AE in machine learning systems, examine emotion-modulated architectures, and summarise mechanisms for modelling and integrating AE into future AI. We also explore the ethical implications and safety risks associated with `emotional' AGI, while concluding with our opinion on how AE could be beneficial in the future.