University of Oulu

Adaptation through a Stochastic Evolutionary Neuron Migration Process

Saved in:
Author: Haverinen, Janne1
Organizations: 1University of Oulu, Faculty of Technology, Department of Electrical and Information Engineering
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 14.8 MB)
Persistent link: http://urn.fi/urn:isbn:9514273079
Language: English
Published: 2004
Publish Date: 2004-03-23
Thesis type: Doctoral Dissertation
Defence Note: Academic Dissertation to be presented with the assent of the Faculty of Technology, University of Oulu, for public discussion in Kuusamonsali (Auditorium YB210), Linnanmaa, on March 23th, 2004, at 12 noon.
Reviewer: Professor Jarmo Alander
Professor Rüdiger Dillmann
Description:

Abstract

Artificial Life is an interdisciplinary scientific and engineering enterprise investigating the fundamental properties of living systems through the simulation and synthesis of life-like processes in artificial media. One of the avenues of investigation is autonomous robots and agents.

Mimicking of the growth and adaptation of a biological neural circuit in an artificial medium is a challenging task owing to our limited knowledge of the complex process taking place in a living organism. By combining several developmental mechanisms, including the chemical, mechanical, genetic, and electrical, researchers have succeeded in developing networks with interesting topology, morphology, and function within Artificial Computational Chemistry. However, most of these approaches still fail to create neural circuits able to solve real problems in perception and robot control.

In this thesis a phenomenological developmental model called a Stochastic Evolutionary Neuron Migration Process (SENMP) is proposed. Employing a spatial encoding scheme with lateral interaction of neurons for artificial neural networks, which represent candidate solutions within a neural network ensemble, neurons of the ensemble form problem-specific spatial patterns with the desired dynamics as they migrate under the selective pressure.

The approach is applied to gain new insights into development, adaptation and plasticity in neural networks and to evolve purposeful behaviors for mobile robots. In addition, the approach is used to study the relationship of spatial patterns, composed of interacting entities, and their dynamics.

The feasibility and advantages of the approach are demonstrated by evolving neural controllers for solving a non-Markovian double pole balancing problem and by evolving controllers that exhibit navigation behavior for simulated and real mobile robots in complex environments. Preliminary results regarding the behavior of the adapting neural network ensemble are also shown and, particularly, a phenomenon exhibiting Hebbian-like dynamics.

This thesis is a step toward a long range goal that aims to create an intelligent robot that is capable of learning complex skills and adapts rapidly to environmental changes.

see all

Series: Acta Universitatis Ouluensis. C, Technica
ISSN-E: 1796-2226
ISBN: 951-42-7307-9
ISBN Print: 951-42-7306-0
Issue: 202
Subjects:
Copyright information: This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.