| Title |
Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges
|
|---|---|
| Published in |
Cognitive Computation, December 2015
|
| DOI | 10.1007/s12559-015-9365-5 |
| Pubmed ID | |
| Authors |
Hussein A. Abbass, Eleni Petraki, Kathryn Merrick, John Harvey, Michael Barlow |
| Abstract |
This paper considers two emerging interdisciplinary, but related topics that are likely to create tipping points in advancing the engineering and science areas. Trusted Autonomy (TA) is a field of research that focuses on understanding and designing the interaction space between two entities each of which exhibits a level of autonomy. These entities can be humans, machines, or a mix of the two. Cognitive Cyber Symbiosis (CoCyS) is a cloud that uses humans and machines for decision-making. In CoCyS, human-machine teams are viewed as a network with each node comprising humans (as computational machines) or computers. CoCyS focuses on the architecture and interface of a Trusted Autonomous System. This paper examines these two concepts and seeks to remove ambiguity by introducing formal definitions for these concepts. It then discusses open challenges for TA and CoCyS, that is, whether a team made of humans and machines can work in fluid, seamless harmony. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Unknown | 114 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 16% |
| Student > Master | 17 | 15% |
| Researcher | 13 | 11% |
| Student > Doctoral Student | 8 | 7% |
| Professor | 5 | 4% |
| Other | 16 | 14% |
| Unknown | 38 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 26 | 23% |
| Engineering | 15 | 13% |
| Psychology | 15 | 13% |
| Business, Management and Accounting | 4 | 3% |
| Social Sciences | 3 | 3% |
| Other | 8 | 7% |
| Unknown | 44 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 47. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 June 2021.
All research outputs
#862,788
of 24,920,664 outputs
Outputs from Cognitive Computation
#3
of 436 outputs
Outputs of similar age
#14,986
of 402,016 outputs
Outputs of similar age from Cognitive Computation
#1
of 12 outputs
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So far Altmetric has tracked 436 research outputs from this source. They receive a mean Attention Score of 2.6. This one has done particularly well, scoring higher than 99% of its peers.
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We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.