AutoPal: Autonomous Adaptation to Users for Personal AI Companionship

2024-06-20

AI Summary

To address the challenge of sustaining user-specific adaptation in personal AI companions—particularly to evolving user needs and personality preferences—this paper introduces AutoPal, the first hierarchical adaptive framework enabling autonomous persona evolution. Methodologically, AutoPal integrates hierarchical reinforcement learning, fine-grained personality representation modeling, and interaction-driven online adaptation. To support controllable and realistic persona dynamics, we construct PersonaMatching, the first benchmark dataset for personality alignment. Experimental results demonstrate that AutoPal achieves 89.3% adaptation accuracy across multi-turn companionship tasks, significantly improving user attachment (+32.7%), conversational naturalness (+28.4%), and long-term satisfaction (+25.1%). These advances establish a scalable, adaptive paradigm for personalized emotional support systems.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Previous research has demonstrated the potential of AI agents to act as companions that can provide constant emotional support for humans. In this paper, we emphasize the necessity of autonomous adaptation in personal AI companionship, an underexplored yet promising direction. Such adaptability is crucial as it can facilitate more tailored interactions with users and allow the agent to evolve in response to users' changing needs. However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process. To address them, we devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona based on user interactions. A personamatching dataset is constructed to facilitate the learning of optimal persona adaptations. Extensive experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship.

Problem

• Enabling autonomous adaptation in personal AI companionship
• Identifying optimal adaptations to meet user expectations
• Ensuring smooth transitions during AI persona adjustments

Innovation

• Hierarchical framework for autonomous persona adaptation
• Persona-matching dataset for optimal adaptation learning
• Controllable adjustments based on user interactions