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Table of Contents
Intro; Acknowledgements; Funding; Contents; 1 From Morphological Computation to Morphological Intelligence; 1.1 How Morphology Lifts the Computational Burden for the Brain; 1.1.1 Morphological Computation; 1.1.2 Morphological Control; 1.1.3 Pre-processing (Sensors); 1.1.4 Post-processing (Actuators); 1.1.5 Brain Layout; 1.1.6 Behaviour-Enabling Physical Processes; 1.2 What is Morphology, Computation, and Morphological Computation?; 1.2.1 How Morphological Computation Has Changed over Time; 1.2.2 Definition of the Term Morphology; 1.2.3 Definition of the Term Computation
1.2.4 What is Morphological Computation?1.3 Morphological Intelligence; 1.4 Organisation of This Book and Main Results; References; 2 Information Theory-A Primer; 2.1 Estimating Probabilities; 2.2 Summary; 2.3 Entropy; 2.4 Mutual Information; 2.5 Conditional Mutual Information; 2.6 Kolmogorov Complexity; 2.7 Causality Versus Correlation; 2.8 Entropy Estimation on Continuous State Spaces; 2.8.1 Mutual Information Estimation on Continuous Data; 2.8.2 Conditional Mutual Information Estimation on Continuous Data; 2.9 Conclusion; References; 3 A Theory of Morphological Intelligence
3.1 Related Work on Formalising Morphological Computation3.1.1 Dynamical Systems Approach to Formalising Morphological Intelligence; 3.2 Causal Model of the Sensorimotor Loop; 3.3 Concept One: Quantifying Morphological Intelligence Based on the Effect of the Action on the World; 3.3.1 Morphological Intelligence as Comparison of Behaviour and Controller Complexity (MIMI); 3.3.2 MICA: A Causal Variation of MIA; 3.3.3 Agent-Intrinsic Variations of MIA and MICA; 3.4 Concept Two: Quantifying Morphological Intelligence as the Contribution of the World to Itself
3.4.1 Information Decomposition of MIW3.4.2 Agent-Intrinsic Variation of MIW; 3.5 Concept Three: Quantifying Morphological Intelligence as Synergy of Body and Brain; 3.5.1 Maximum Entropy Estimation with the Iterative Scaling Algorithm; 3.5.2 Quantifying Unique Information; 3.6 Concept Four: Quantifying Morphological Intelligence as In-Sourceable Computation; 3.7 Concept Five: Quantifying Morphological Intelligence as the Reduction of Computational Cost; 3.7.1 Denavit-Hartenberg Notation; 3.7.2 Denavit-Hartenberg for a Hexapod; References
1.2.4 What is Morphological Computation?1.3 Morphological Intelligence; 1.4 Organisation of This Book and Main Results; References; 2 Information Theory-A Primer; 2.1 Estimating Probabilities; 2.2 Summary; 2.3 Entropy; 2.4 Mutual Information; 2.5 Conditional Mutual Information; 2.6 Kolmogorov Complexity; 2.7 Causality Versus Correlation; 2.8 Entropy Estimation on Continuous State Spaces; 2.8.1 Mutual Information Estimation on Continuous Data; 2.8.2 Conditional Mutual Information Estimation on Continuous Data; 2.9 Conclusion; References; 3 A Theory of Morphological Intelligence
3.1 Related Work on Formalising Morphological Computation3.1.1 Dynamical Systems Approach to Formalising Morphological Intelligence; 3.2 Causal Model of the Sensorimotor Loop; 3.3 Concept One: Quantifying Morphological Intelligence Based on the Effect of the Action on the World; 3.3.1 Morphological Intelligence as Comparison of Behaviour and Controller Complexity (MIMI); 3.3.2 MICA: A Causal Variation of MIA; 3.3.3 Agent-Intrinsic Variations of MIA and MICA; 3.4 Concept Two: Quantifying Morphological Intelligence as the Contribution of the World to Itself
3.4.1 Information Decomposition of MIW3.4.2 Agent-Intrinsic Variation of MIW; 3.5 Concept Three: Quantifying Morphological Intelligence as Synergy of Body and Brain; 3.5.1 Maximum Entropy Estimation with the Iterative Scaling Algorithm; 3.5.2 Quantifying Unique Information; 3.6 Concept Four: Quantifying Morphological Intelligence as In-Sourceable Computation; 3.7 Concept Five: Quantifying Morphological Intelligence as the Reduction of Computational Cost; 3.7.1 Denavit-Hartenberg Notation; 3.7.2 Denavit-Hartenberg for a Hexapod; References