Application-Driven Value Alignment in Agentic AI Systems: Survey and Perspectives
🤖 AI Summary
Value alignment in AI agents has become increasingly critical; under the Agentic AI paradigm, autonomous decision-making and multi-agent collaboration in complex environments must conform to human values and societal norms.
Method: We propose a three-tiered (macro–meso–micro) hierarchical framework of value principles, introduce the first application-oriented taxonomy for agent value alignment, and systematically model value coordination mechanisms in multi-agent cooperation. Through systematic literature review, hierarchical modeling, scenario-based analysis, and evaluation framework design, we construct a comprehensive knowledge graph spanning theoretical foundations, application scenarios, and technical methods.
Contribution/Results: We establish the first integrated evaluation framework for value alignment—unifying benchmark datasets and evaluation metrics—and distill six key directions for future research. This work provides both theoretical foundations and practical guidelines for developing trustworthy Agentic AI systems.
Application Category
📝 Abstract
The ongoing evolution of AI paradigms has propelled AI research into the Agentic AI stage. Consequently, the focus of research has shifted from single agents and simple applications towards multi-agent autonomous decision-making and task collaboration in complex environments. As Large Language Models (LLMs) advance, their applications become more diverse and complex, leading to increasingly situational and systemic risks. This has brought significant attention to value alignment for AI agents, which aims to ensure that an agent's goals, preferences, and behaviors align with human values and societal norms. This paper reviews value alignment in agent systems within specific application scenarios. It integrates the advancements in AI driven by large models with the demands of social governance. Our review covers value principles, agent system application scenarios, and agent value alignment evaluation. Specifically, value principles are organized hierarchically from a top-down perspective, encompassing macro, meso, and micro levels. Agent system application scenarios are categorized and reviewed from a general-to-specific viewpoint. Agent value alignment evaluation systematically examines datasets for value alignment assessment and relevant value alignment methods. Additionally, we delve into value coordination among multiple agents within agent systems. Finally, we propose several potential research directions in this field.
Problem
Research questions and friction points this paper is trying to address.
Ensuring AI agent goals align with human values
Addressing value alignment in multi-agent systems
Evaluating value alignment methods and datasets
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical value principles organization
Multi-agent value coordination
Systematic value alignment evaluation
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