Predicting the Intended Action using Internal Simulation of Perception

Zahra Gharaee

2022

Abstract

This article proposes an architecture, which allows the prediction of intention by internally simulating perceptual states represented by action pattern vectors. To this end, associative self-organising neural networks (A-SOM) is utilised to build a hierarchical cognitive architecture for recognition and simulation of the skeleton based human actions. The abilities of the proposed architecture in recognising and predicting actions is evaluated in experiments using three different datasets of 3D actions. Based on the experiments of this article, applying internally simulated perceptual states represented by action pattern vectors improves the performance of the recognition task in all experiments. Furthermore, internal simulation of perception addresses the problem of having limited access to the sensory input, and also the future prediction of the consecutive perceptual sequences. The performance of the system is compared and discussed with similar architecture using self-organizing neural networks (SOM).

Paper Citation

in Harvard Style

Gharaee Z. (2022). Predicting the Intended Action using Internal Simulation of Perception. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-547-0, pages 626-635. DOI: 10.5220/0010977600003116

in Bibtex Style

@conference{icaart22,
author={Zahra Gharaee},
title={Predicting the Intended Action using Internal Simulation of Perception},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2022},
pages={626-635},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010977600003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Predicting the Intended Action using Internal Simulation of Perception
SN - 978-989-758-547-0
AU - Gharaee Z.
PY - 2022
SP - 626
EP - 635
DO - 10.5220/0010977600003116