Abstract
The use of virtual agents (bots) has become essential for providing online assistance to customers. However, even though a lot of effort has been dedicated to the research, development, and deployment of such virtual agents, customers are frequently frustrated with the interaction with the virtual agent and require a human instead. We suggest that a holistic approach, combining virtual agents and human operators working together, is the path to providing satisfactory service. However, implementing such a holistic customer service system will not, and cannot, be achieved using any single AI technology or branch. Rather, such a system will inevitably require the integration of multiple and diverse AI technologies, including natural language processing, multi-agent systems, machine learning, reinforcement learning, and behavioral cloning; in addition to integration with other disciplines such as psychology, business, sociology, economics, operation research, informatics, computer-human interaction, and more. As such, we believe this customer service application offers a rich domain for experimentation and application of multidisciplinary AI. In this paper, we introduce the holistic customer service application and discuss the key AI technologies and disciplines required for a successful AI solution for this setting. For each of these AI technologies, we outline the key scientific questions and research avenues stemming from this setting. We demonstrate that integrating technologies from different fields can lead to a cost-effective successful customer service center. The challenge is that there is a need for several communities, each with its own language and modeling techniques, different problem-solving methods, and different evaluation methodologies, all of which need to work together. Real cooperation will require the formation of joint methodologies and techniques that could improve the service to customers, but, more importantly, open new directions in cooperation of diverse communities toward solving joint difficult tasks.
| Original language | English |
|---|---|
| Title of host publication | AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations |
| Editors | Brian Williams, Yiling Chen, Jennifer Neville |
| Publisher | AAAI press |
| Pages | 15393-15401 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781577358800 |
| DOIs | |
| State | Published - 27 Jun 2023 |
| Event | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023 |
Publication series
| Name | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
|---|---|
| Volume | 37 |
Conference
| Conference | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
|---|---|
| Country/Territory | United States |
| City | Washington |
| Period | 7/02/23 → 14/02/23 |
Bibliographical note
Publisher Copyright:Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding
This research was funded in part by JPMorgan Chase & Co and by the IBM Global University Program Academic Award. Any views or opinions expressed herein are solely those of the authors listed, and may differ from the views and opinions expressed by JPMorgan Chase & Co. or its affiliates. This material is not a product of the Research Department of J.P. Morgan Securities LLC. This material should not be construed as an individual recommendation for any particular client and is not intended as a recommendation of particular securities, financial instruments or strategies for a particular client. This material does not constitute a solicitation or offer in any jurisdiction.
| Funders | Funder number |
|---|---|
| JPMorgan Chase & Co | |
| International Business Machines Corporation |