AGENTIC-SYSTEMSJuly 7, 20261 min read

Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making

Researchers have proposed the Human-Centric Reflective Architecture (HCRA) to address the challenges of human over-reliance or under-reliance on AI recommendations. By integrating reinforcement learning with linguistic feedback, HCRA aligns AI agents with human expectations to enhance collaborative decision-making.

Computer Science > Artificial Intelligence

Title:Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making

View PDF HTML (experimental)Abstract:The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback. Concurrently, it seeks to align decisions with human expectations, preferences, and needs while mitigating risks associated with AI non-determinism. However, humans frequently over- or under-rely on AI recommendations, and current AI systems remain poorly calibrated to human expectations. To address these challenges, we introduce a human-AI collaborative decision-making framework designed to augment human capabilities and align AI agents with human preferences and expectations. Specifically, this paper (a) formulates the collaborative decision-making task as a stochastic game between an AI agent and a human player, and (b) proposes the Human-Centric Reflective Architecture (HCRA), which integrates human-calibrated models with reinforcement learning agents that leverage linguistic feedback in an iterative, reflective process. Evaluation results demonstrate that HCRA enhances decision-making effectiveness and delivers high-quality recommendations.

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Source: arXiv cs.AI Recent

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