Name
Abstract | ||
|---|---|---|
We present the Associative Self-Organizing Map (A-SOM) and propose that it could be used to predict an agent's intentions by internally simulating the behaviour likely to follow initial movements. The A-SOM is a neural network that develops a representation of its input space without supervision, while simultaneously learning to associate its activity with an arbitrary number of additional (possibly delayed) inputs. We argue that the A-SOM would be suitable for the prediction of the likely continuation of the perceived behaviour of an agent by learning to associate activity patterns over time, and thus a way to read its intentions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34274-5_32 | BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES 2012 |
Field | DocType | Volume |
Associative property,Computer science,Continuation,Artificial intelligence,Artificial neural network,Machine learning | Conference | 196 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Magnus Johnsson | 1 | 99 | 13.51 |
| Miriam Buonamente | 2 | 9 | 2.30 |