Internal Representations in Evolutionary Robotics

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Table of contents

1 Introduction 
1.1 Context
1.1.1 Artificial Intelligence and Cognitive Science
1.1.2 Evolutionary Robotics
1.2 Problem
1.3 Outline
2 Background 
2.1 Evolutionary Algorithms
2.1.1 History
2.1.2 Principle
2.1.3 Multi-Objective
2.2 Evolutionary Robotics
2.2.1 Introduction
2.2.2 Behavior
2.2.3 Fitness Functions
2.2.4 Conclusions
2.3 Artificial Neural Networks
2.3.1 Neuron models
2.3.2 Neural Networks
2.3.3 Evolution and Artificial neural networks
2.3.4 Evolution of Artificial Neural Network Topology
2.3.5 Conclusions
3 Internal Representations 
3.1 Internal Representations and Cognitive Behaviors
3.1.1 Internal Representations
3.1.2 Examples of Reactive and Cognitive Behaviors
3.1.3 Internal Representations in Computational Neuroscience
3.2 Internal Representations in Evolutionary Robotics
3.2.1 Representation-less ER tasks
3.2.2 Representation-Hungry Tasks
3.2.3 Tasks with unclear Representation needs
3.2.4 Conclusion
3.3 Testing Characteristics of Internal Representations
3.3.1 Measuring Representations in the literature
3.3.2 Properties of Internal Representations
3.3.3 Quantitative tests
3.3.4 Qualitative tests
3.4 Analysis over Existing Networks
3.4.1 Representations in classic networks
3.4.2 Visual Attention
3.4.3 ER task: Hard Ball-Collecting Task
3.4.4 Conclusion
4 Behavioral Consistency Method 
4.1 Introduction
4.2 Working Memory Experiment
4.2.1 T-Maze navigation task
4.2.2 Methods
4.2.3 Results
4.2.4 Conclusion
4.3 Behavioral Consistency Method
4.3.1 Related Work
4.3.2 Behavioral Consistency Method
4.4 Attention Focus
4.4.1 Experimental Setup
4.4.2 Results
4.5 Action Selection
4.5.1 Experimental setup
4.5.2 Results
4.6 Conclusion
5 Environmental Pressures and emergence of Internal Representations 
5.1 Selective Pressures for the emergence of Internal Representations
5.1.1 Task and World Complexity
5.1.2 Helper Objectives and Specific Pressures
5.2 Circular Maze Evolutionary Robotics Protocol
5.2.1 Goal-oriented and Novelty Objective
5.2.2 Behavioral Consistency Objective and Environmental Pressures
5.2.3 Representation Tests
5.2.4 Setups and Selective Pressures
5.3 Results: Emergence of Internal Representations under Environmental
5.3.1 Impact of Selection Pressures
5.3.2 Resulting Architectures
5.3.3 Lineage and selection for Internal Representation
5.4 Conclusion
6 Discussion and Perspectives 
6.1 Contributions
6.1.1 Definition of Internal Representations
6.1.2 Behavioral Consistency Method
6.1.3 Emergence of Internal Representations under multiple selective pressures
6.2 Discussion and Perspectives
6.2.1 Classification of representations and tests
6.2.2 Behavioral Consistency Method versus standard evaluation .
6.2.3 Emergence of Internal Models
6.2.4 New Selective Pressures
6.2.5 Neural Network Encodings
7 Conclusion 
A Appendix 
A.1 DNN and EvoNeuro2 Parameters
A.2 Additional Lineage
A.3 Neural Networks
A.4 Chronograms
A.5 Comparison between BCO and Standard Evaluation
Bibliography