Advertisement

AI-Based Technical Approach for Designing Mobile Decision Aids

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1033)

Abstract

Conversational Voice User Interfaces (VUIs) help us in performing tasks in a wide range of domains these days. While there have been several efforts around designing dialogue systems and conversation flows, little information is available about technical concepts to extract critical information for addressing the users’ needs. For conversational VUIs to function appropriately as a decision aid, artificial intelligence (AI) that recognizes and supports diverse user decision strategies is a critical need. Following the design principle proposed by Kwon et al. [1] regarding the conversational flow between the user and conversational VUI, we developed an AI-based mobile-decision-aid (MODA) that predictively models and addresses users’ decision strategies to facilitate users’ in-store shopping decision process. In this paper, technical details about how MODA processes users’ natural language queries and generate the most appropriate and intelligent recommendations have been discussed. This developmental approach provides broad implications to conversational VUIs for diverse complex decision-making contexts and decision-makers with a critical need for decision assistants.

Keywords

Decision aid systems Conversational agent Voice User Interface Artificial intelligence 

Notes

Acknowledgements

This material is based in part upon work supported by the National Science Foundation under Grant Numbers IIS-1527182 and IIS-1527302; the Alabama Agricultural Experiment Station; and the Hatch program of the National Institute of Food and Agriculture, U.S. Department of Agriculture. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies acknowledged above.

References

  1. 1.
    Kwon, W.-S., Chattaraman, V., Ross, K., Alikhademi, K., Gilbert, J.E.: Modeling conversational flows for in-store mobile decision aids. In: Stephanidis, C. (ed.) HCI 2018. CCIS, vol. 852, pp. 302–308. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92285-0_42CrossRefGoogle Scholar
  2. 2.
    Chattaraman, V., Kwon, W.-S., Eugene, W., Gilbert, J.: Developing and validating a naturalistic decision model for intelligent language-based decision aids. Hum. Factors Ergon. Soc. Annu. Meet. 61(1), 176–177 (2017)CrossRefGoogle Scholar
  3. 3.
    Qiu, L., Benbasat, I.: Evaluating anthropomorphic product recommendation agents: a social relationship perspective to designing information systems. J. Manag. Inf. Syst. 25(4), 145–182 (2009).  https://doi.org/10.2753/MIS0742-1222250405CrossRefGoogle Scholar
  4. 4.
    Qiu, L., Jiang, Z., Benbasat, I.: Real experience in a virtual store: designing for presence in online shopping. In: Proceedings of the PACIS 2006, p. 93 (2006)Google Scholar
  5. 5.
    Andersen, V., Hansen, C.B., Andersen, H.H.K.: Evaluation of agents and study of end-user needs and behaviour for e-commerce. COGITO focus group experiment (2001)Google Scholar
  6. 6.
    Benbasat, I., Wang, W.: Trust in and adoption of online recommendation agents. J. Assoc. Inf. Syst. 6(3), 4 (2005)Google Scholar
  7. 7.
    Gnewuch, U., Morana, S., Maedche, A.: Towards designing cooperative and social conversational agents for customer service (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of FloridaGainesvilleUSA
  2. 2.Auburn UniversityAuburnUSA

Personalised recommendations