库伊:复杂能源环境下的人工智能聊天机器人

Gihan Gamage, Nishan Mills, Prabod Rathnayaka, Andrew Jennings, D. Alahakoon
{"title":"库伊:复杂能源环境下的人工智能聊天机器人","authors":"Gihan Gamage, Nishan Mills, Prabod Rathnayaka, Andrew Jennings, D. Alahakoon","doi":"10.1109/HSI55341.2022.9869464","DOIUrl":null,"url":null,"abstract":"Contemporary energy platforms are leveraging advanced data management and Artificial Intelligence (AI) capabilities in response to the increasing complexity of energy systems and grids. Despite these advances, it is a non-trivial and challenging task to support the decision-making needs of the human operators of such complex energy-related implementations. Conversational agents or chatbots are a potential emerging technology that can be utilized to address this challenge. Although there is a large body of literature on chatbots in general, they are not robust as they rely on predefined conversational pathways that are inadequate to efficiently address the complexities of dynamic data spaces in energy platforms. The capability of generating answers in real-time by communicating with the dynamic dataspace is crucial as energy management decisions are real-time and time sensitive. In this paper, we present the design and development of Cooee, a chatbot for conversational engagement with the dynamic data spaces of complex energy environments. Cooee leverages state-of-art language models along with rule-based language processing methods for a conversational interaction with dynamic data spaces, which consequently supports and enables decision-making by human experts. We have developed Cooee as a standalone application and then integrated into a real-world energy AI platform deployed within a multi-campus tertiary education institution setting. Cooee was empirically evaluated in this setting and compared with several state-of-the-art Q&A approaches.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Cooee: An Artificial Intelligence Chatbot for Complex Energy Environments\",\"authors\":\"Gihan Gamage, Nishan Mills, Prabod Rathnayaka, Andrew Jennings, D. Alahakoon\",\"doi\":\"10.1109/HSI55341.2022.9869464\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Contemporary energy platforms are leveraging advanced data management and Artificial Intelligence (AI) capabilities in response to the increasing complexity of energy systems and grids. Despite these advances, it is a non-trivial and challenging task to support the decision-making needs of the human operators of such complex energy-related implementations. Conversational agents or chatbots are a potential emerging technology that can be utilized to address this challenge. Although there is a large body of literature on chatbots in general, they are not robust as they rely on predefined conversational pathways that are inadequate to efficiently address the complexities of dynamic data spaces in energy platforms. The capability of generating answers in real-time by communicating with the dynamic dataspace is crucial as energy management decisions are real-time and time sensitive. In this paper, we present the design and development of Cooee, a chatbot for conversational engagement with the dynamic data spaces of complex energy environments. Cooee leverages state-of-art language models along with rule-based language processing methods for a conversational interaction with dynamic data spaces, which consequently supports and enables decision-making by human experts. We have developed Cooee as a standalone application and then integrated into a real-world energy AI platform deployed within a multi-campus tertiary education institution setting. Cooee was empirically evaluated in this setting and compared with several state-of-the-art Q&A approaches.\",\"PeriodicalId\":282607,\"journal\":{\"name\":\"2022 15th International Conference on Human System Interaction (HSI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 15th International Conference on Human System Interaction (HSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HSI55341.2022.9869464\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 15th International Conference on Human System Interaction (HSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HSI55341.2022.9869464","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

当代能源平台正在利用先进的数据管理和人工智能(AI)能力来应对日益复杂的能源系统和电网。尽管取得了这些进步,但在如此复杂的能源相关实施过程中,支持人类操作员的决策需求仍然是一项艰巨而具有挑战性的任务。会话代理或聊天机器人是一种潜在的新兴技术,可以用来解决这一挑战。尽管有大量关于聊天机器人的文献,但它们并不健壮,因为它们依赖于预定义的会话路径,这些路径不足以有效地解决能源平台中动态数据空间的复杂性。通过与动态数据空间通信实时生成答案的能力至关重要,因为能源管理决策是实时且时间敏感的。在本文中,我们介绍了Cooee的设计和开发,Cooee是一个用于与复杂能源环境的动态数据空间进行对话的聊天机器人。Cooee利用最先进的语言模型和基于规则的语言处理方法,与动态数据空间进行对话交互,从而支持并实现人类专家的决策。我们将Cooee开发为一个独立的应用程序,然后将其集成到一个部署在多校区高等教育机构环境中的真实能源人工智能平台中。在这种情况下对Cooee进行了经验性评估,并与几种最先进的问答方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooee: An Artificial Intelligence Chatbot for Complex Energy Environments
Contemporary energy platforms are leveraging advanced data management and Artificial Intelligence (AI) capabilities in response to the increasing complexity of energy systems and grids. Despite these advances, it is a non-trivial and challenging task to support the decision-making needs of the human operators of such complex energy-related implementations. Conversational agents or chatbots are a potential emerging technology that can be utilized to address this challenge. Although there is a large body of literature on chatbots in general, they are not robust as they rely on predefined conversational pathways that are inadequate to efficiently address the complexities of dynamic data spaces in energy platforms. The capability of generating answers in real-time by communicating with the dynamic dataspace is crucial as energy management decisions are real-time and time sensitive. In this paper, we present the design and development of Cooee, a chatbot for conversational engagement with the dynamic data spaces of complex energy environments. Cooee leverages state-of-art language models along with rule-based language processing methods for a conversational interaction with dynamic data spaces, which consequently supports and enables decision-making by human experts. We have developed Cooee as a standalone application and then integrated into a real-world energy AI platform deployed within a multi-campus tertiary education institution setting. Cooee was empirically evaluated in this setting and compared with several state-of-the-art Q&A approaches.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信