Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being

• URL: http://arxiv.org/abs/2502.17172v1
• Date: Mon, 24 Feb 2025 14:07:53 GMT
• Title: Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being
• Authors: Bin Yin, Chong-Yi Liu, Liya Fu, Jinkun Zhang,
• Abstract summary: We propose a teleology-driven affective computing framework that unifies major emotion theories.<n>We advocate for creating a "dataverse" of personal affective events.<n>We introduce a meta-reinforcement learning paradigm to train agents in simulated environments.
• Score: 0.1636303041090359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents toward long-term human well-being. To address this, we propose a teleology-driven affective computing framework that unifies major emotion theories (basic emotion, appraisal, and constructivist approaches) under the premise that affect is an adaptive, goal-directed process that facilitates survival and development. Our framework emphasizes aligning agent responses with both personal/individual and group/collective well-being over extended timescales. We advocate for creating a "dataverse" of personal affective events, capturing the interplay between beliefs, goals, actions, and outcomes through real-world experience sampling and immersive virtual reality. By leveraging causal modeling, this "dataverse" enables AI systems to infer individuals' unique affective concerns and provide tailored interventions for sustained well-being. Additionally, we introduce a meta-reinforcement learning paradigm to train agents in simulated environments, allowing them to adapt to evolving affective concerns and balance hierarchical goals - from immediate emotional needs to long-term self-actualization. This framework shifts the focus from statistical correlations to causal reasoning, enhancing agents' ability to predict and respond proactively to emotional challenges, and offers a foundation for developing personalized, ethically aligned affective systems that promote meaningful human-AI interactions and societal well-being.

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