Semantic Support for Computer-Human Interaction: Intuitive 3DVirtual Tools for Surface Deformation in CAD

2010 ◽  
pp. 645-654 ◽  
Author(s):  
Ioana Ciuciu ◽  
Robert Meersman ◽  
Estelle Perrin ◽  
Frédéric Danesi
Keyword(s):  
Surface Deformation ◽  
Human Interaction

Human Interaction: Understanding Is Not Enough

1974 ◽  
Vol 19 (7) ◽  
pp. 539-540
Author(s):  
NEWTON MARGULIES
Keyword(s):  
Human Interaction

Review of Interpersonal dynamics: Essays and readings on human interaction.

1975 ◽  
Vol 20 (7) ◽  
pp. 594-595
Author(s):  
ROBERT D. LANGSTON
Keyword(s):  
Human Interaction ◽  
Interpersonal Dynamics

A Comprehensive Model for Medical Information Processing

1983 ◽  
Vol 22 (03) ◽  
pp. 124-130 ◽  
Author(s):  
J. H. Bemmel
Keyword(s):  
Intensive Care ◽  
Information Processing ◽  
Medical Information ◽  
Computer Applications ◽  
Human Interaction ◽  
Comprehensive Model ◽  
Computers In Medicine ◽  
Medical Information Systems ◽  
Research And Education ◽  
The Many

At first sight, the many applications of computers in medicine—from payroll and registration systems to computerized tomography, intensive care and diagnostics—do make a rather chaotic impression. The purpose of this article is to propose a scheme or working model for putting medical information systems in order. The model comprises six »levels of complexity«, running parallel to dependence on human interaction. Several examples are treated to illustrate the scheme. The reason why certain computer applications are more frequently used than others is analyzed. It has to be strongly considered that the differences in complexity and dependence on human involvement are not accidental but fundamental. This has consequences for research and education which are also discussed.

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

Sign in / Sign up

Export Citation Format

Share Document