Discriminating and Simulating Actions with the Associative Self-Organizing Map
(2015) In Connection Science 27(2). p.118-136- Abstract
- Abstract in Undetermined
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... (More) - Abstract in Undetermined
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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5011242
- author
- Buonamente, Miriam ; Dindo, Haris and Johnsson, Magnus LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- associative self-organising map, action recognition, internal, simulation, intention understanding, neural network
- in
- Connection Science
- volume
- 27
- issue
- 2
- pages
- 118 - 136
- publisher
- Taylor & Francis
- external identifiers
-
- wos:000354115100003
- scopus:84929292171
- ISSN
- 0954-0091
- DOI
- 10.1080/09540091.2015.1025571
- project
- Ikaros: An infrastructure for system level modelling of the brain
- language
- English
- LU publication?
- yes
- id
- 20693821-2f4e-4f89-8d31-ff11ec3fafa0 (old id 5011242)
- date added to LUP
- 2016-04-01 10:51:37
- date last changed
- 2022-03-12 17:39:45
@article{20693821-2f4e-4f89-8d31-ff11ec3fafa0, abstract = {{Abstract in Undetermined<br/>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.}}, author = {{Buonamente, Miriam and Dindo, Haris and Johnsson, Magnus}}, issn = {{0954-0091}}, keywords = {{associative self-organising map; action recognition; internal; simulation; intention understanding; neural network}}, language = {{eng}}, number = {{2}}, pages = {{118--136}}, publisher = {{Taylor & Francis}}, series = {{Connection Science}}, title = {{Discriminating and Simulating Actions with the Associative Self-Organizing Map}}, url = {{http://dx.doi.org/10.1080/09540091.2015.1025571}}, doi = {{10.1080/09540091.2015.1025571}}, volume = {{27}}, year = {{2015}}, }