How analysts think: A preliminary study of human needs and demands for AI-based conversational agents-Reference-Cited by-同舟云学术

How analysts think: A preliminary study of human needs and demands for AI-based conversational agents

Published:2019-11 Issue:1 Volume:63 Page:178-182
ISSN:2169-5067
Container-title:Proceedings of the Human Factors and Ergonomics Society Annual Meeting
language:en
Short-container-title:Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Author:

Hepenstal Sam¹,Wong B.L. William²,Zhang Leishi²,Kodogoda Neesha²

Affiliation:

1. Defence Science Technology Laboratory, Porton Down, Salisbury SP4 0JQ, England

2. Middlesex University London, The Burroughs, Hendon, London NW4 4BT, England

Abstract

For conversational agents to provide benefit to intelligence analysis they need to be able to recognise and respond to the analysts intentions. Furthermore, they must provide transparency to their algorithms and be able to adapt to new situations and lines of inquiry. We present a preliminary analysis as a first step towards developing conversational agents for intelligence analysis: that of understanding and modeling analyst intentions so they can be recognised by conversational agents. We describe in-depth interviews conducted with experienced intelligence analysts and implications for designing conversational agent intentions.

Publisher

SAGE Publications

Subject

General Medicine,General Chemistry

Link

http://journals.sagepub.com/doi/pdf/10.1177/1071181319631333

Reference11 articles.

1. Hepenstal Sam, Kodagoda Neesha, Zhang Leishi, Paudyal Pragya, Wong B. L. William. “Algorithmic Transparency of Conversational Agents.” In Joint Proceedings of the ACM IUI 2019 Workshops, Los Angeles, USA, March 20, 2019.

2. Critical decision method for eliciting knowledge

3. Making Sense of Sensemaking 2: A Macrocognitive Model

4. Task taxonomy for graph visualization

5. Spoken dialogue technology

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Impact of System Transparency on Analytical Reasoning;Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19

2. A Granular Computing Approach to Provide Transparency of Intelligent Systems for Criminal Investigations;Studies in Computational Intelligence;2021

3. Providing a foundation for interpretable autonomous agents through elicitation and modeling of criminal investigation pathways;Proceedings of the Human Factors and Ergonomics Society Annual Meeting;2020-12

4. Pan;Proceedings of the 25th International Conference on Intelligent User Interfaces Companion;2020-03-13