用大模型辅助构建语义网多智能体系统，降低开发门槛。
LLM-Assisted Modeling of Semantic Web-Enabled Multi-Agents Systems with AJAN
- 结合大语言模型与语义网标准，自动生成RDF/SPARQL行为代码。
- 解决智能体建模中URI拼写错误和复杂查询学习难的问题。
- 适合非专业开发者快速上手语义网多智能体系统开发。
现有语义网标准已支持多智能体应用的实现，AJAN框架基于这些标准构建多智能体系统。其中，智能体知识以RDF/RDFS和OWL表示，行为模型通过行为树（Behavior Trees）和SPARQL查询进行定义与操作。然而，在实际应用中，如何正确编写基于RDF/RDFS和SPARQL的智能体行为仍是一大挑战。例如，处理URI时易出错，而在大规模环境中编写复杂SPARQL查询需较高学习成本。本文提出一个集成开发环境，旨在克服上述建模障碍，并通过引入大语言模型（LLM）扩展用户群体，使更广泛的开发者能借助语言模型完成智能体工程任务。
原文摘要 · Abstract (English)
There are many established semantic Web standards for implementing multi-agent driven applications. The AJAN framework allows to engineer multi-agent systems based on these standards. In particular, agent knowledge is represented in RDF/RDFS and OWL, while agent behavior models are defined with Behavior Trees and SPARQL to access and manipulate this knowledge. However, the appropriate definition of RDF/RDFS and SPARQL-based agent behaviors still remains a major hurdle not only for agent modelers in practice. For example, dealing with URIs is very error-prone regarding typos and dealing with complex SPARQL queries in large-scale environments requires a high learning curve. In this paper, we present an integrated development environment to overcome such hurdles of modeling AJAN agents and at the same time to extend the user community for AJAN by the possibility to leverage Large Language Models for agent engineering.
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