Self-Programming Machines (II): Network of Self-Programming Machines Driving an Ashby Homeostat

The progress in artificial intelligence enables us to conceive adaptive systems whose characteristics are nearer and nearer to those of living beings. These characteristics though depend on ingenious choices by the designer of these systems: Initial conditions, parameters, optimisation functions, gr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Acta biotheoretica Jg. 51; H. 4; S. 265 - 276
1. Verfasser: Moulin, J.-P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Springer Nature B.V 01.01.2003
Schlagworte:
Artificial Intelligence
Automation - instrumentation
Computer Communication Networks
Computer Systems
Feedback
Finite Element Analysis
Homeostasis
Humans
Neural Networks (Computer)
Potentiometry - instrumentation
Software
Studies
User-Computer Interface
ISSN:0001-5342, 1572-8358
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The progress in artificial intelligence enables us to conceive adaptive systems whose characteristics are nearer and nearer to those of living beings. These characteristics though depend on ingenious choices by the designer of these systems: Initial conditions, parameters, optimisation functions, gradient and measure of fitness within the environment. Nevertheless, in living systems which are non-finalist, there are no programmers or designers to conceive of such ingenious choices. Our paper "Self-Programming Machines (I)" presents a non-finalist model since initial states and functions are randomly chosen at the beginning and once and for all. In spite of the fact that they are non-finalist, these machines always stabilise at fixed points when they are connected to an external process. This paper studies the dynamics of a mono-layered network of self-programming machines driving a real device. "the Ashby homeostat", and shows the striking properties of such networks. This system stabilises only at fixed points even if it is subjected to small perturbations or intentional breakdowns such as a reversal of power supply or disconnection of one or several motors. Real and simulated experiences are compared and theoretical results are demonstrated.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0001-5342
1572-8358
DOI:10.1023/B:ACBI.0000003983.86071.a0