Abstract

Controlled natural language (CNL) has great potential to support human–machine interaction (HMI) because it provides an information representation that is both human readable and machine processable. We investigated the effectiveness of a CNL-based conversational interface for HMI in a behavioral experiment called simple human experiment regarding locally observed collective knowledge ( Sherlock ). In Sherlock , individuals acted in groups to discover and report information to the machine using natural language (NL), which the machine then processed into CNL. The machine fused responses from different users to form a common operating picture, a dashboard showing the level of agreement for distinct information. To obtain information to add to this dashboard, users explored the real world in a simulated crowdsourced sensing scenario. This scenario represented a simplified controlled analog for tactical intelligence (i.e., direct intelligence of the environment), which is key for rapidly planning military, law enforcement, and emergency operations. Overall, despite close to zero training, 74% of the users inputted NL that was machine interpretable and addressed the assigned tasks. An experimental manipulation aimed to increase user–machine interaction, however, did not improve performance as hypothesized. Nevertheless, results indicate that the conversational interface may be effective in assisting humans with collection and fusion of information in a crowdsourcing context.

Highlights

  • C ONTROLLED natural languages (CNLs) support human– machine interaction (HMI) or collaboration by providing an information representation that aims to be human readable and writable, while being machine processable [1]

  • Individual users acting in groups collaboratively built a shared CNL knowledge base (KB) via a process of inputting natural language (NL) and confirming equivalent CNL suggested by the agent

  • The primary results were that the conversational agent had high usability, supporting the first hypothesis that it would act as an effective cognitive artifact

Read more

Summary

Introduction

C ONTROLLED natural languages (CNLs) support human– machine interaction (HMI) or collaboration by providing an information representation that aims to be human readable and writable, while being machine processable [1]. The research design was motivated by a need to support users conducting information tasks in situ, for example, providing reports from the field on current events, seeking information relevant to their current situation, or instructing a range of “smart” devices—such as sensing systems or robots—to assist them. This motivation is consistent with the U.S Department of Defence’s Third Offset Strategy

Objectives
Methods
Results
Discussion
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.