Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects

TL;DR

This survey comprehensively reviews the development of emotionally intelligent agents, covering emotion understanding, expression, challenges, and future prospects, emphasizing multimodal data and generative technologies.

Contribution

It provides a holistic overview of core components, challenges, and future directions in affective computing, integrating emotion recognition, elicitation, and expression across modalities.

Findings

Addresses emotion understanding via multimodal data processing

Analyzes key challenges in affective system development

Highlights potential of generative technologies for future advances

Abstract

The development of agents with emotional intelligence is becoming increasingly vital due to their significant role in human-computer interaction and the growing integration of computer systems across various sectors of society. Affective computing aims to design intelligent systems that can recognize, evoke, and express human emotions, thereby emulating human emotional intelligence. While previous reviews have focused on specific aspects of this field, there has been limited comprehensive research that encompasses emotion understanding, elicitation, and expression, along with the related challenges. This survey addresses this gap by providing a holistic overview of core components of artificial emotion intelligence. It covers emotion understanding through multimodal data processing, as well as affective cognition, which includes cognitive appraisal, emotion mapping, and adaptive modulation in decision-making, learning, and reasoning. Additionally, it addresses the synthesis of emotional expression across text, speech, and facial modalities to enhance human-agent interaction. This paper identifies and analyzes the key challenges and issues encountered in the development of affective systems, covering state-of-the-art methodologies designed to address them. Finally, we highlight promising future directions, with particular emphasis on the potential of generative technologies to advance affective computing.