Name
Abstract | ||
|---|---|---|
In order to develop artificial agents operating in complex ever-changing environments, advanced technical memory systems are required. At this juncture, two central questions are which information needs to be stored and how it is represented. On the other hand, cognitive psychology provides methods to measure the structure of mental representations in humans. But the nature and the characteristics of the underlying representations are largely unknown. We propose to use feature selection methods to determine adequate technical features for approximating the structure of mental representations found in humans. Although this approach does not allow for drawing conclusions transferable to humans, it constitutes an excellent basis for creating technical equivalents of mental representations. |
Year | Venue | Keywords |
|---|---|---|
2011 | Lecture Notes in Computer Science | Feature selection,Mental representations,Memory |
Field | DocType | Volume |
Juncture,Information needs,Feature selection,Computer science,Artificial intelligence,Memory systems,Machine learning,Mental representation | Conference | 7064 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
8 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marko Tscherepanow | 1 | 150 | 10.53 |
| Marco Kortkamp | 2 | 12 | 1.95 |
| Sina Kühnel | 3 | 0 | 0.34 |
| Jonathan Helbach | 4 | 0 | 0.68 |
| Christoph Schütz | 5 | 0 | 0.68 |
| Thomas Schack | 6 | 33 | 7.51 |