TY - GEN
T1 - HACO
T2 - 13th Innovations in Software Engineering Conference, ISEC 2020
AU - Dubey, Alpana
AU - Abhinav, Kumar
AU - Jain, Sakshi
AU - Arora, Veenu
AU - Puttaveerana, Asha
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/2/27
Y1 - 2020/2/27
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Collaboration
KW - Intelligent agents
KW - Software framework
UR - https://www.scopus.com/pages/publications/85082668318
U2 - 10.1145/3385032.3385044
DO - 10.1145/3385032.3385044
M3 - Conference contribution
AN - SCOPUS:85082668318
T3 - ACM International Conference Proceeding Series
BT - iSOFT - Proceedings of the 13th Innovations in Software Engineering Conference (Formerly known as India Software Engineering Conference), ISEC 2020
PB - Association for Computing Machinery
Y2 - 27 February 2020 through 29 February 2020
ER -