Achieving robust human–computer communication

doi:10.1006/ijhc.1997.0186

International Journal of Human-Computer Studies

Volume 48, Issue 5, May 1998, Pages 681-704

https://doi.org/10.1006/ijhc.1997.0186 Get rights and content

Abstract

This paper describes a computational approach to robust human–computer interaction. The approach relies on an explicit, declarative representation of the content and structure of the interaction that a computer system builds over the course of the interaction. In this paper, we will show how this representation allows the system to recognize and repair misunderstandings between the human and the computer. We demonstrate the utility of the representations by showing how they facilitate the repair process.

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Cited by (4)

The influence of visual feedback and gender dynamics on performance, perception and communication strategies in CSCW
2017, International Journal of Human Computer Studies
Citation Excerpt :
Effectiveness was assessed through miscommunication rates. The study adapted McRoy's HCI-oriented framework and classified miscommunication into three types: execution errors; non-understandings; and incorrect instructions (McRoy, 1998). Execution errors – instances where the follower deviated from the described route – were recorded by comparing the route executed by the follower (as determined by system logs) with the route described by the instructor.
The effects of gender in human communication and human-computer interaction are well-known, yet little is understood about how it influences performance in the complex, collaborative tasks in computer-mediated settings – referred to as Computer-Supported Collaborative Work (CSCW) – that are increasingly fundamental to the way in which people work. In such tasks, visual feedback about objects and events is particularly valuable because it facilitates joint reference and attention, and enables the monitoring of people's actions and task progress. As such, software to support CSCW frequently provides shared visual workspace. While numerous studies describe and explain the impact of visual feedback in CSCW, research has not considered whether there are differences in how females and males use it, are aided by it, or are affected by its absence. To address these knowledge gaps, this study explores the effect of gender – and its interactions within pairs – in CSCW, with and without visual feedback. An experimental study is reported in which mixed-gender and same-gender pairs communicate to complete a collaborative navigation task, with one of the participants being under the impression that s/he is interacting with a robot (to avoid gender-related social preconceptions). The study analyses performance, perceptions and communication strategies. As predicted, there was a significant benefit associated with visual feedback in terms of language economy and efficiency. However, it was also found that visual feedback may be disruptive to task performance, because it relaxes the users’ precision criteria and inflates their assumptions of shared perspective. While no actual performance difference was found between males and females in the navigation task, females rated their own performance less positively than did males. In terms of communication strategies, males had a strong tendency to introduce novel vocabulary when communication problems occurred, while females exhibited more conservative behaviour. When visual feedback was removed, females adapted their strategies drastically and effectively, increasing the quality and specificity of the verbal interaction, repeating and re-using vocabulary, while the behaviour of males remained consistent. These results are used to produce design recommendations for CSCW systems that will suit users of both genders and enable effective collaboration.
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Dynamic Context Generation for Natural Language Understanding: A Multifaceted Knowledge Approach
2003, IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.
ARGUER: Using argument schemas for argument detection and rebuttal in dialogs
1999, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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International Journal of Human-Computer Studies

Regular ArticleAchieving robust human–computer communication

Abstract

The influence of visual feedback and gender dynamics on performance, perception and communication strategies in CSCW

Equuleus: Presentation from Legacy Documents

Dynamic Context Generation for Natural Language Understanding: A Multifaceted Knowledge Approach

ARGUER: Using argument schemas for argument detection and rebuttal in dialogs

Regular Article
Achieving robust human–computer communication