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Abstract
Contents
Abstract
Contents
Introduction
Background
Research questions
Motivation and rationale
Intelligence defined
Artificial Intelligence
Level of representation: connectionist versus symbolic AI
Motivation
Biology's drivers for intelligence
Substrates
The Brain
The neuron and neuronal structures
Substrates in general
Evolution
Development
Evolution and complexity
Evolutionary Anthropology view
Artificial Implementation
Artificial counterparts of key mechanisms
Evolutionary algorithms
Genetic algorithms
Artificial Neural Networks
Evolving neural networks (EA and ANN combined)
Example implementations
Evolution of virtual morphology and control
The implementation
Lessons learned
Relation to other work
Babybot: an artificial developing robotic agent
The implementation
Lessons learned and relation to other work
Barriers to implementation
A suitable substrate
Implementation of plasticity
Massive parallelism: up to
neurons
Rich connectedness:
to
synapses per neuron
General intelligence
Emergence of intelligence
The computability of thought
History
Roger Penrose's View
Criticism
Open ended evolution
Conclusions
The Turing test
Design testing procedure
Relevance and objections
Bibliography
About this document ...
Erik de Bruijn 2007-10-19
