Author:
Gabriele Peters
Affiliation:
University of Hagen, Germany
Keyword(s):
Self-learning systems, Autonomous learning, Machine learning, Emergence, Self-organization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bio-Inspired and Humanoid Robotics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Self-Organization and Emergence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this position paper the broad issue of learning and self-organisation is addressed. I deal with the question how biological and technological information processing systems can autonomously acquire cognitive capabilities only from data available in the environment. In the main part I claim six qualities that are, in my opinion, necessary qualities of self-learning systems. These qualities are (1) hierarchical processing, (2) emergence on all levels of hierarchy, (3) multi-directional information transfer between the levels of hierarchy, (4) generalization from few examples, (5) exploration, and (6) adaptivity. I try to support my considerations by theoretical reflections as well as by an informal introduction of a self-learning system that features these qualities and displays promising behavior in object recognition applications. Although this paper has more the character of a brainstorming the proposed qualities can be regarded as roadmap for problems to be addressed in future r
esearch in the field of autonomous learning.
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