Analysis of Dynamic Function Allocation between Human Operators and Automation Systems

2008 ◽  
Author(s):  
So-Young Kim ◽  
Seung Man Lee ◽  
Eric Johnson
Keyword(s):  
Function Allocation ◽  
Dynamic Function ◽  
Automation Systems ◽  
Human Operators

Function allocation for multi-agent systems and middleware in industrial automation systems

ETFA2011 ◽  
2011 ◽  
Author(s):  
Leon Urbas ◽  
Annett Krause ◽  
Stephan Pech ◽  
Peter Gohner
Keyword(s):  
Multi Agent Systems ◽  
Industrial Automation ◽  
Agent Systems ◽  
Function Allocation ◽  
Automation Systems ◽  
Multi Agent ◽  
Industrial Automation Systems

scholarly journals Putting Time (Back) Into Dynamic Function Allocation

2003 ◽  
Vol 47 (3) ◽  
pp. 488-492 ◽  
Author(s):  
Michael Hildebrandt ◽  
Michael Harrison
Keyword(s):  
Function Allocation ◽  
Dynamic Function

Roadmap for the next generation of dynamic function allocation theories and strategies

2010 ◽  
Vol 21 (1) ◽  
pp. 14-28 ◽  
Author(s):  
Amit V. Lagu ◽  
Steven J. Landry
Keyword(s):  
Next Generation ◽  
Function Allocation ◽  
Dynamic Function

Survey of Future Railroad Operations and the Role of Automation

2017 ◽  
Vol 2608 (1) ◽  
pp. 10-18
Author(s):  
James D. Brooks ◽  
Hannah Groshong ◽  
Andrew Liu ◽  
Paul Houpt ◽  
Chuck Oman
Keyword(s):  
Development Process ◽  
Future Research ◽  
Perceived Effectiveness ◽  
Current Development ◽  
Railroad Industry ◽  
The Past ◽  
Automation Systems ◽  
Human Operators ◽  
Automation Technology

In the past decade, freight rail automation systems have made significant advances. The objective of this work was to elicit ideas from the railroad industry about future automation systems and their impact on future operating configurations (such as the roles of human operators). A Dephi survey was administered in two rounds to industry leaders (Class I railroad managers and General Electric transportation senior engineers). The industry was generally found to be open to new operating configurations and to see increasing automation technology as key to achieving future benefits. However, there are significant concerns around training, deskilling, and the current development process. Several solutions to each of these problems were ranked by participants in order of perceived effectiveness. The implications for the development of rail technology and opportunities for future research are discussed.

Developing Dynamic Function Allocation for Future Naval Systems

1997 ◽  
Vol 41 (2) ◽  
pp. 1047-1051
Author(s):  
Catherine A. Cook ◽  
Colin Corbridge ◽  
Catherine A. Morgan ◽  
Andrew J. Tattersall
Keyword(s):  
Optimal Allocation ◽  
Command And Control ◽  
Human Sciences ◽  
Knowledge Based ◽  
Experimental Environment ◽  
Function Allocation ◽  
Dynamic Function ◽  
Potential Benefits ◽  
Experimental Findings ◽  
And Control

The DERA Centre for Human Sciences is investigating function allocation for future Naval systems with the aim of providing optimal allocation of functions within “lean” manned ships. Existing methods of allocating functions between human and machine were developed around old, “dumb” technology, the inherent assumption being that once this process had been undertaken, the outcome remained fixed. However, for future systems, knowledge-based technology provides the potential for functions to be allocated dynamically, according lo the operational context. As part of a programme to examine the potential of dynamic function allocation, a series of six experiments utilising the Multi-Attribute Task Battery (MATB) were conducted, and a brief overview of the experimental findings is reported. The main focus of this paper is to describe a new experimental environment which has been developed to address the limitations in the MATB, particularly the relevance of these tasks to Naval systems. This is an abstraction of a Naval Anti-Air Warfare Controller's task, incorporating dynamic function allocation. It will facilitate an in-depth investigation of the potential benefits of this technology for the Naval Command and Control domain.

Trusting Automation: Designing for Responsivity and Resilience

2021 ◽  
pp. 001872082110099
Author(s):  
Erin K. Chiou ◽  
John D. Lee
Keyword(s):  
Individual Characteristics ◽  
System Level ◽  
Organizational Contexts ◽  
Automation Systems ◽  
Trust In Automation ◽  
Human Operators ◽  
Narrative Literature Review ◽  
Relational Framework ◽  
Future Work ◽  
Research And Design

Objective This paper reviews recent articles related to human trust in automation to guide research and design for increasingly capable automation in complex work environments. Background Two recent trends—the development of increasingly capable automation and the flattening of organizational hierarchies—suggest a reframing of trust in automation is needed. Method Many publications related to human trust and human–automation interaction were integrated in this narrative literature review. Results Much research has focused on calibrating human trust to promote appropriate reliance on automation. This approach neglects relational aspects of increasingly capable automation and system-level outcomes, such as cooperation and resilience. To address these limitations, we adopt a relational framing of trust based on the decision situation, semiotics, interaction sequence, and strategy. This relational framework stresses that the goal is not to maximize trust, or to even calibrate trust, but to support a process of trusting through automation responsivity. Conclusion This framing clarifies why future work on trust in automation should consider not just individual characteristics and how automation influences people, but also how people can influence automation and how interdependent interactions affect trusting automation. In these new technological and organizational contexts that shift human operators to co-operators of automation, automation responsivity and the ability to resolve conflicting goals may be more relevant than reliability and reliance for advancing system design. Application A conceptual model comprising four concepts—situation, semiotics, strategy, and sequence—can guide future trust research and design for automation responsivity and more resilient human–automation systems.

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