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Internal Simulation of an Agent`s Intentions

Johnsson, Magnus LU and Buonamente, Miriam LU (2013) Biologically Inspired Cognitive Architectures 2012: Third Annual Meeting of the BICA Society In Biologically Inspired Cognitive Architectures 2012 (Advances in Intelligent Systems and Computing) 196. p.175-176
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.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Biologically Inspired Cognitive Architectures 2012 (Advances in Intelligent Systems and Computing)
editor
Chella, Antonio and
volume
196
pages
175 - 176
publisher
Springer
conference name
Biologically Inspired Cognitive Architectures 2012: Third Annual Meeting of the BICA Society
external identifiers
  • wos:000313923100032
  • scopus:84870772018
ISSN
2194-5357
ISBN
978-3-642-34274-5
DOI
10.1007/978-3-642-34274-5_32
project
Cognition, Communication and Learning
language
English
LU publication?
yes
id
7f9e5a68-3c8d-4a08-9fc0-299b482f36f2 (old id 3437751)
date added to LUP
2013-02-01 13:27:49
date last changed
2017-01-01 06:33:11
@inproceedings{7f9e5a68-3c8d-4a08-9fc0-299b482f36f2,
  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.},
  author       = {Johnsson, Magnus and Buonamente, Miriam},
  booktitle    = {Biologically Inspired Cognitive Architectures 2012 (Advances in Intelligent Systems and Computing)},
  editor       = {Chella, Antonio},
  isbn         = {978-3-642-34274-5},
  issn         = {2194-5357},
  language     = {eng},
  pages        = {175--176},
  publisher    = {Springer},
  title        = {Internal Simulation of an Agent`s Intentions},
  url          = {http://dx.doi.org/10.1007/978-3-642-34274-5_32},
  volume       = {196},
  year         = {2013},
}