Towards digital cognitive clones for the decision-makers : adversarial training experiments
Abstract
There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one’s responsible representative when the human is not available. Pi-Mind (“Patented Intelligence”) is a technology, which enables “cloning” cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveillance over the conveyors. The digital component of the environment contains special modifications of Generative Adversarial Networks, which include a human-operator as a trainer, an autonomous Pi-Mind clone as a trainee (a discriminator) and a smart digital adversary as a challenger (generator of sophisticated decision situations, emergencies and attacks, which supposedly catalyzes the cloning process).
See presentation slides: https://ai.it.jyu.fi/ISM-2020-Pi-Mind.pptx
Main Authors
Format
Conferences
Conference paper
Published
2021
Series
Subjects
Publication in research information system
Publisher
Elsevier
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202102231753Käytä tätä linkitykseen.
Review status
Peer reviewed
ISSN
1877-0509
DOI
https://doi.org/10.1016/j.procs.2021.01.155
Conference
International Conference on Industry 4.0 and Smart Manufacturing
Language
English
Published in
Procedia Computer Science
Is part of publication
ISM 2020 : Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing
Citation
- Golovianko, M., Gryshko, S., Terziyan, V., & Tuunanen, T. (2021). Towards digital cognitive clones for the decision-makers : adversarial training experiments. In F. Longo, M. Affenzeller, & A. Padovano (Eds.), ISM 2020 : Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (180, pp. 180-189). Elsevier. Procedia Computer Science. https://doi.org/10.1016/j.procs.2021.01.155
Copyright© 2021 the Authors