New research explores emergent emotional preference in AI agents

Researchers have developed a computational framework for emergent emotional preference in artificial agents, inspired by the goal-directed theory of emotion. This system uses reinforcement learning to allow a high-level goal to autonomously generate state-dependent preferences over competing lower-level objectives, rather than relying on externally defined priorities. Experiments in simulated environments demonstrated that the learned preference function can dynamically switch priorities based on context, exhibit graded trade-offs, and maintain temporal persistence, outperforming fixed or manually set preference strategies. AI

IMPACT This research could lead to more adaptable and human-like decision-making in AI agents by enabling them to dynamically adjust their priorities.

RANK_REASON The cluster contains a single academic paper detailing a new computational framework for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• Bibliographic Explorer
• CatalyzeX Code Finder for Papers
• Connected Papers
• DagsHub
• Emotional Preferences as Goal-Priority Regulation
• Gotit.pub
• Hugging Face
• Influence Flower
• Litmaps
• machine learning
• ScienceCast
• scite Smart Citations

• paper
• safety

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →