{"type":"ScholarlyArticle","title":"Horae: A Domain-Agnostic Language for Automated Service Regulation","authors":[{"name":"Yutao Sun","slug":"yutao-sun","given":"Yutao","family":"Sun","affiliation":""},{"name":"Mingshuai Chen","slug":"mingshuai-chen","given":"Mingshuai","family":"Chen","affiliation":""},{"name":"Tiancheng Zhao","slug":"tiancheng-zhao","given":"Tiancheng","family":"Zhao","affiliation":""},{"name":"Kangjia Zhao","slug":"kangjia-zhao","given":"Kangjia","family":"Zhao","affiliation":""},{"name":"He Li","slug":"he-li","given":"He","family":"Li","affiliation":""},{"name":"Jintao Chen","slug":"jintao-chen","given":"Jintao","family":"Chen","affiliation":""},{"name":"Zhongyi Wang","slug":"zhongyi-wang","given":"Zhongyi","family":"Wang","affiliation":""},{"name":"Liqiang Lu","slug":"liqiang-lu","given":"Liqiang","family":"Lu","affiliation":""},{"name":"Xinkui Zhao","slug":"xinkui-zhao","given":"Xinkui","family":"Zhao","affiliation":""},{"name":"Shuiguang Deng","slug":"shuiguang-deng","given":"Shuiguang","family":"Deng","affiliation":""},{"name":"Jianwei Yin","slug":"jianwei-yin","given":"Jianwei","family":"Yin","affiliation":""}],"abstract":"Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domains and thus are difficult to generalize in an automated manner. This paper presents Horae, a unified specification language for modeling (multimodal) regulation rules across a diverse set of domains. We showcase how Horae facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named RuleGPT that automates the Horae modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. The feasibility and effectiveness of our framework are demonstrated over a benchmark of various real-world regulation domains. In particular, we show that our open-sourced, fine-tuned RuleGPT with 7B parameters suffices to outperform GPT-3.5 and perform on par with GPT-4o.","doi":"10.24963/ijcai.2025/1039","url":"https://ijcai.org/proceedings/2025/1039","pdf":"https://ijcai.org/proceedings/2025/1039.pdf","markdown":"https://ijcai.org/proceedings/2025/1039.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1039","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1039,"pages":{"first":9349,"last":9356},"keywords":["Domain-specific AI4Tech: AI4Regulation","Advanced AI4Tech: Generative and LLMs-driven AI4Tech"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}