规范挖掘,识别和检测:一个系统的文献审查
Benoît Alcaraz1, Yazan Mualla2, Sukriti Bhattacharya3
1Department of Computer Science, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Frontiers in artificial intelligence
|March 16, 2026
概括
本综述探讨了多代理系统中的规范识别,这对于代理合作至关重要. 目前的方法需要提高社会意识的自主系统的可扩展性和现实世界的使用.
科学领域:
- 人工智能的人工智能
- 多代理系统 多代理系统
- 社会意识的计算社会意识的计算
背景情况:
- 规范对于在多代理系统中规范行为至关重要,促进合作和解决冲突.
- 了解规范识别是开发复杂可靠的自主系统的关键.
- 现有研究提出了在代理社会中对规范管理的多种方法.
研究的目的:
- 系统地审查和分类现有的标准识别在多代理系统的方法.
- 评估这些方法的有效性,特别是在动态和不确定的环境中.
- 确定当前的局限性,并建议未来的研究方向.
主要方法:
- 系统性文献审查方法.
- 对35项关于规范识别的研究进行分析.
- 标准检测,合成和适应方法的分类.
主要成果:
- 识别和分类各种标准识别技术.
- 在模拟的动态和不确定的环境中评估方法的性能.
- 突出了当前方法在可扩展性,适应性和现实世界的适用性方面的重大差距.
结论:
- 当前的规范识别方法在复杂的现实场景中面临挑战.
- 未来的研究应该侧重于整合大型语言模型 (LLM) 和跨学科合作.
- 推进社会意识的自主系统需要更强大,更适应性的规范识别策略.
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