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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Toward Self-Referential Autonomous Learning of Object and Situation Models
|
|---|---|
| Published in |
Cognitive Computation, April 2016
|
| DOI | 10.1007/s12559-016-9407-7 |
| Pubmed ID | |
| Authors |
Florian Damerow, Andreas Knoblauch, Ursula Körner, Julian Eggert, Edgar Körner |
| Abstract |
Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Netherlands | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 4% |
| Russia | 1 | 4% |
| Unknown | 22 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 17% |
| Student > Doctoral Student | 3 | 13% |
| Student > Bachelor | 3 | 13% |
| Student > Master | 2 | 8% |
| Researcher | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 8 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 33% |
| Engineering | 6 | 25% |
| Agricultural and Biological Sciences | 1 | 4% |
| Psychology | 1 | 4% |
| Mathematics | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 7 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 03 August 2016.
All research outputs
#17,008,464
of 24,998,746 outputs
Outputs from Cognitive Computation
#165
of 436 outputs
Outputs of similar age
#186,264
of 304,783 outputs
Outputs of similar age from Cognitive Computation
#7
of 19 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 436 research outputs from this source. They receive a mean Attention Score of 2.6. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 304,783 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.