Name
Abstract | ||
|---|---|---|
We propose a system able to represent others' actions as well as to internally simulate their likely continuation from a partial observation. The approach presented here is the first step towards a more ambitious goal of endowing an artificial agent with the ability to recognise and predict others' intentions. Our approach is based on the associative self-organising map, a variant of the self-organising map capable of learning to associate its activity with different inputs over time, where inputs are processed observations of others' actions. We have evaluated our system in two different experimental scenarios obtaining promising results: the system demonstrated an ability to learn discriminable representations of actions, to recognise novel input, and to simulate the likely continuation of partially seen actions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1080/09540091.2015.1025571 | Connection Science |
Keywords | Field | DocType |
action recognition,associative self-organising map,internal simulation,intention understanding,neural network | Associative property,Computer science,Action recognition,Continuation,Artificial intelligence,Artificial neural network,Machine learning,Self organising maps | Journal |
Volume | Issue | ISSN |
27 | 2 | 0954-0091 |
Citations | PageRank | References |
3 | 0.42 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miriam Buonamente | 1 | 9 | 2.30 |
| Haris Dindo | 2 | 125 | 17.49 |
| Magnus Johnsson | 3 | 99 | 13.51 |