将农民信任度量化融入多目标强化学习，提升农业AI决策的可接受性。
Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management
- 构建基于能力、善意、诚信的可信度数学模型，量化农民对AI施肥策略的信任
- 通过农民调研发现关键认知偏差，并将其嵌入强化学习优化过程
- 让AI推荐既科学高效，又符合农户经验与当地实践，适合推广到智慧农业
精准农业借助人工智能（AI）在遥感、智能灌溉、施肥管理及作物模拟等方面展现出巨大潜力，显著提升农业效率与可持续性。强化学习（RL）在优化产量与资源管理方面已超越传统方法。然而，算法建议与农民实际经验、地方知识和传统做法之间的差距，限制了AI的广泛应用。为此，本研究聚焦人机交互（HAII），强调透明度、可用性与信任。采用包含能力、善意与诚信的成熟信任框架，构建新颖数学模型，量化农民对基于AI的施肥策略的信心。通过对农户开展调研，识别出关键认知偏差，并将其整合进信任模型，进而融入多目标强化学习框架。与以往方法不同，本研究将信任直接嵌入策略优化过程，确保AI建议兼具技术可靠性、经济可行性、情境适应性与社会可接受性。通过技术性能与以人为本的信任相协同，推动农业领域更广泛地采纳AI技术。
原文摘要 · Abstract (English)
Precision agriculture, enhanced by artificial intelligence (AI), offers promising tools such as remote sensing, intelligent irrigation, fertilization management, and crop simulation to improve agricultural efficiency and sustainability. Reinforcement learning (RL), in particular, has outperformed traditional methods in optimizing yields and resource management. However, widespread AI adoption is limited by gaps between algorithmic recommendations and farmers' practical experience, local knowledge, and traditional practices. To address this, our study emphasizes Human-AI Interaction (HAII), focusing on transparency, usability, and trust in RL-based farm management. We employ a well-established trust framework - comprising ability, benevolence, and integrity - to develop a novel mathematical model quantifying farmers' confidence in AI-based fertilization strategies. Surveys conducted with farmers for this research reveal critical misalignments, which are integrated into our trust model and incorporated into a multi-objective RL framework. Unlike prior methods, our approach embeds trust directly into policy optimization, ensuring AI recommendations are technically robust, economically feasible, context-aware, and socially acceptable. By aligning technical performance with human-centered trust, this research supports broader AI adoption in agriculture.
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