|
Title
|
|
|
|
Autonomous parsing of behavior in a multi-agent setting
|
|
|
Author
|
|
|
|
|
|
|
Abstract
|
|
|
|
| Imitation learning is a promising route to instruct robotic multi-agent systems. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman Net) can be used to extract meaningful chunks from a continuous sequence of observed actions. Results suggest that, even in spite of the high level of task specific noise, Elman nets can be used for isolating re-occurring action patterns in robots. Limitations and future directions are discussed. |
|
|
|
Language
|
|
|
|
English
|
|
|
Source (book)
|
|
|
|
Proceedings of the 9th International Conference on Artificial Intelligence and Soft Computing
|
|
|
Publication
|
|
|
|
S.l.
:
2008
|
|
|
DOI
|
|
|
|
10.1007/978-3-540-69731-2_112
|
|
|
Full text (Publisher's DOI)
|
|
|
|
|
|