Alpana Dubey, K. Abhinav, Sakshi Jain, Veenu Arora, Asha Puttaveerana
{"title":"HACO","authors":"Alpana Dubey, K. Abhinav, Sakshi Jain, Veenu Arora, Asha Puttaveerana","doi":"10.1145/3385032.3385044","DOIUrl":null,"url":null,"abstract":"We witnessed great advancement in artificial intelligence (AI) powered technologies over the past few decades. Wide use of AI technologies has led to the creation of an ecosystem where human and AI systems are partners, complementing each other with their strengths. To build a successful human-AI team, there are several considerations, including context awareness, effective communication, pro-activeness, etc. In this paper, we present a taxonomy of human-AI teaming concepts. We extend a multi-agent framework, Java Agent Development Framework (JADE), to support the proposed taxonomy. Our solution framework, Human-AI Collaboration (HACO), enables a model-driven development of human-AI teaming systems through graphical user interface. In this paper, we present the solution architecture for extending JADE with human-AI teaming taxonomy. A user study performed to assess the usefulness of HACO, shows that HACO is a promising framework. We evaluated the proposed framework by developing a set of use cases for a contact center and observed a signification reduction in the overall development effort. The framework video can be viewed at https://youtu.be/lNyrrk8dMqU.","PeriodicalId":382901,"journal":{"name":"Proceedings of the 13th Innovations in Software Engineering Conference on Formerly known as India Software Engineering Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 13th Innovations in Software Engineering Conference on Formerly known as India Software Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3385032.3385044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
We witnessed great advancement in artificial intelligence (AI) powered technologies over the past few decades. Wide use of AI technologies has led to the creation of an ecosystem where human and AI systems are partners, complementing each other with their strengths. To build a successful human-AI team, there are several considerations, including context awareness, effective communication, pro-activeness, etc. In this paper, we present a taxonomy of human-AI teaming concepts. We extend a multi-agent framework, Java Agent Development Framework (JADE), to support the proposed taxonomy. Our solution framework, Human-AI Collaboration (HACO), enables a model-driven development of human-AI teaming systems through graphical user interface. In this paper, we present the solution architecture for extending JADE with human-AI teaming taxonomy. A user study performed to assess the usefulness of HACO, shows that HACO is a promising framework. We evaluated the proposed framework by developing a set of use cases for a contact center and observed a signification reduction in the overall development effort. The framework video can be viewed at https://youtu.be/lNyrrk8dMqU.