可持续性推系统:概述和研究问题
Alexander Felfernig1, Manfred Wundara2, Thi Ngoc Trang Tran1
1Applied Software Engineering & AI, Institute of Software Technology, Graz University of Technology, Graz, Austria.
Frontiers in big data
|November 15, 2023
概括
使用人工智能如机器学习的推系统有助于实现可持续发展目标 (SDGs). 本综述探讨了它们的当前应用和全球进步的未来研究方向.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 可持续性科学 可持续性科学
背景情况:
- 联合国的可持续发展目标 (SDGs) 为应对关键挑战提供了一个全球框架.
- 实现可持续发展目标需要创新的技术解决方案来支持各种组织和个人.
- 人工智能 (AI) 为解决复杂的可持续性问题提供了强大的工具.
研究的目的:
- 审查推者系统在支持可持续发展目标实现方面的当前最先进应用.
- 突出AI技术的作用,包括机器学习和可解释AI (XAI),在可持续性推系统中.
- 识别和讨论这个跨学科领域的开放挑战和未来研究方向.
主要方法:
- 对用于可持续发展发展目标的推者系统的文献综述.
- 对集成到推系统中的AI技术进行分析,例如机器学习,XAI,基于案例的推理和约束解决.
- 综合发现,绘制当前应用的地图,并确定研究缺口.
主要成果:
- 推系统有效地利用人工智能,为实现特定的可持续发展目标提供与用户相关的替代方案.
- 像机器学习和XAI这样的关键AI技术对于这些系统的功能和可解释性至关重要.
- 推者系统的应用涵盖了各种可持续发展目标,为实现目标提供了实际支持.
结论:
- 推系统是推进可持续发展发展目标的有价值的人工智能驱动工具.
- 需要进一步的研究来探索新的AI方法,并解决推系统应用在可持续性方面的现有挑战.
- 在这个领域的持续发展可以为全球保护地球,消除贫困和和平的努力做出重大贡献.
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