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Autonomous Robots
Erschienen in: Autonomous Robots 2/2021

05.01.2021 | Original Research

A hierarchical representation of behaviour supporting open ended development and progressive learning for artificial agents

verfasst von: François Suro, Jacques Ferber, Tiberiu Stratulat, Fabien Michel

Erschienen in: Autonomous Robots | Ausgabe 2/2021

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Abstract

One of the challenging aspects of open ended or lifelong agent development is that the final behaviour for which an agent is trained at a given moment can be an element for the future creation of one, or even several, behaviours of greater complexity, whose purpose cannot be anticipated. In this paper, we present modular influence network design (MIND), an artificial agent control architecture suited to open ended and cumulative learning. The MIND architecture encapsulates sub behaviours into modules and combines them into a hierarchy reflecting the modular and hierarchical nature of complex tasks. Compared to similar research, the main original aspect of MIND is the multi layered hierarchy using a generic control signal, the influence, to obtain an efficient global behaviour. This article shows the ability of MIND to learn a curriculum of independent didactic tasks of increasing complexity covering different aspects of a desired behaviour. In so doing we demonstrate the contributions of MIND to open-ended development: encapsulation into modules allows for the preservation and re-usability of all the skills acquired during the curriculum and their focused retraining, the modular structure serves the evolving topology by easing the coordination of new sensors, actuators and heterogeneous learning structures.
Vorheriger Artikel An improved kinematic model for skid-steered wheeled platforms
Nächster Artikel Robust circumnavigation of a heterogeneous multi-agent system

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Fußnoten
1
Objects can be naturally split into parts and sub-parts, complex features and simple features (Kruger et al. 2013).
 
6
OpenCV computer vision library: https://​opencv.​org/​.
 
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Metadaten
Titel
A hierarchical representation of behaviour supporting open ended development and progressive learning for artificial agents
verfasst von
François Suro
Jacques Ferber
Tiberiu Stratulat
Fabien Michel
Publikationsdatum
05.01.2021
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 2/2021
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-020-09960-7

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