Active Inference First International Workshop, IWAI 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14, 2020, Proceedings

• Main Authors: Verbelen, Tim, Lanillos, Pablo, Buckley, Christopher L, De Boom, Cedric
• Format: eBook Conference Proceeding
• Language: English
• Published: Cham Springer Nature 2020; Springer International Publishing AG; Springer International Publishing
• Edition: 1
• Series: Communications in Computer and Information Science
• Subjects: Artificial Intelligence; Computer Appl. in Social and Behavioral Sciences; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science; Information Systems and Communication Service; Numeric Computing; Numerical analysis-Congresses; Probability and Statistics in Computer Science; Special computer methods

This book constitutes the refereed proceedings of the First International Workshop on Active Inference, IWAI 2020, co-located with ECML/PKDD 2020, held in Ghent, Belgium, in September 2020. The 13 full papers along with 6 short papers were thoroughly reviewed and selected from 25 submissions. They a...

Table of Contents:

• 3 Results -- 4 Conclusions -- References -- Learning Where to Park -- 1 Introduction -- 2 Problem Statement -- 3 Model Specification -- 3.1 The Physical Model -- 3.2 The Target Model -- 4 Experimental Validation -- 4.1 Setup -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Active Inference: Theory and Biology -- Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference -- 1 Integrated World Modeling Theory (IWMT) Summarized: Combining the Free Energy Principle and Active Inference (FEP-AI) Framework with Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT) -- 2 Integrated World Modeling Theory (IWMT) Implemented -- 2.1 Mechanisms of Predictive Processing: Folded Variational Autoencoders (VAEs) and Self-organizing Harmonic Modes (SOHMs) -- 2.2 A Model of Episodic Memory and Imagination -- 2.3 Brains as Hybrid Machine Learning Architectures -- 2.4 Conclusion: Functions of Basic Phenomenal Consciousness? -- 3 Appendices -- 3.1 Appendix 1: A Model of Goal-Oriented Behavior with Hippocampal Orchestration -- 3.2 Appendix 2: The VAE-GAN Brain? -- References -- Confirmatory Evidence that Healthy Individuals Can Adaptively Adjust Prior Expectations and Interoceptive Precision Estimates -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- References -- Visual Search as Active Inference -- 1 Introduction -- 2 Problem Statement: Formalizing Visual Search as Accuracy Seeking -- 2.1 Visual Search Task -- 2.2 Central Processing -- 2.3 Accuracy Map -- 3 Principles: Supervised Learning of Action Selection -- 3.1 Peripheral Visual Processing -- 3.2 Motor Control -- 3.3 Higher Level Inference: Choosing the Processing Pathway -- 3.4 Learning the Accuracy Map -- 4 Results -- 5 Discussion and Perspectives -- References
• Sophisticated Affective Inference: Simulating Anticipatory Affective Dynamics of Imagining Future Events -- 1 Introduction -- 2 Methods -- 3 Results -- References -- Causal Blankets: Theory and Algorithmic Framework -- 1 Introduction -- 1.1 Markov Blankets -- 1.2 Computational Mechanics, Causal States, and Epsilon-Machines -- 1.3 Contribution -- 2 Causal Blankets as Informational Boundaries -- 3 Integrated Information Transcends the Blankets -- 4 Conclusion -- A Proofs -- References -- Author Index
• Intro -- Preface -- Organization -- Contents -- Active Inference and Continuous Control -- On the Relationship Between Active Inference and Control as Inference -- 1 Introduction -- 2 Formalism -- 3 Control as Inference -- 4 Active Inference -- 5 Encoding Value -- 6 Discussion -- References -- Active Inference or Control as Inference? A Unifying View -- 1 Introduction -- 2 Background -- 2.1 Problem Formulation -- 2.2 Variational Inference for Latent Variable Models -- 3 Active Inference -- 3.1 Free Energy of the Future -- 3.2 Active Inference in Practice -- 4 Control as Inference -- 4.1 Linear Gaussian Inference and Linear Quadratic Control -- 5 The Unifying View: Control of the Observations -- 6 Conclusion -- References -- Active Inference for Fault Tolerant Control of Robot Manipulators with Sensory Faults -- 1 Introduction -- 2 Problem Statement -- 3 A Fault Tolerant Scheme Based on Active Inference -- 4 Simulation Results -- 5 Discussion and Conclusion -- References -- A Worked Example of Fokker-Planck-Based Active Inference -- 1 Introduction -- 2 The Fokker-Planck Equation for Dynamical Systems -- 3 Laplace-Encoded Free Energy and Generative Models -- 4 A Worked Example -- 5 Results -- 6 Discussion -- References -- Dynamics of a Bayesian Hyperparameter in a Markov Chain -- 1 Introduction -- 2 IID Parameter Inference -- 2.1 Fully Observable Markov Chain -- References -- Online System Identification in a Duffing Oscillator by Free Energy Minimisation -- 1 Introduction -- 2 System -- 3 Identification -- 3.1 Free Energy Minimisation -- 3.2 Factor Graphs and Message Passing -- 4 Experiment -- 4.1 1-Step Ahead Prediction Error -- 4.2 Simulation Error -- 5 Discussion -- 5.1 Related Work -- 6 Conclusion -- References -- Hierarchical Gaussian Filtering of Sufficient Statistic Time Series for Active Inference -- 1 Introduction
• 2 Bayesian Inference Reduced to Mean-Tracking -- 2.1 Mean Tracking and Exponential Weighting -- 2.2 A Conjugate Prior Which Reduces Bayesian Inference to Mean Tracking for Exponential Families -- 3 Predictive Distributions -- 4 Filtering of Sufficient Statistics for Non-stationary Input Distributions -- 5 Discussion -- References -- Active Inference and Machine Learning -- Deep Active Inference for Partially Observable MDPs -- 1 Introduction -- 2 Deep Active Inference Model -- 3 Experimental Setup -- 4 Results -- 5 Conclusion -- References -- Sleep: Model Reduction in Deep Active Inference -- 1 Introduction -- 2 Deep Active Inference -- 3 Latent Space Dimensionality Reduction and Sleep -- 4 Experimental Setup -- 5 Results -- 6 Conclusion -- References -- A Deep Active Inference Model of the Rubber-Hand Illusion -- 1 Introduction -- 2 Deep Active Inference Model -- 2.1 Generative Model Learning -- 2.2 Modelling Visuo-Tactile Stimulation Synchrony -- 3 Experimental Setup -- 4 Results -- 5 Conclusion -- References -- You Only Look as Much as You Have To -- 1 Introduction -- 2 Active Inference -- 3 Environment and Approach -- 4 Experiments -- 4.1 Exploring Behaviour -- 4.2 Goal Seeking Behaviour -- 5 Conclusion -- A The Generative Model -- References -- Modulation of Viability Signals for Self-regulatory Control -- 1 Introduction -- 2 Preliminaries -- 2.1 Model-Free Surprisal Minimization -- 2.2 Expected Free Energy -- 3 Adaptive Control via Self-regulation -- 3.1 Case Study -- 3.2 Evaluation -- 4 Discussion -- A Expected Free Energy with Measurements v -- B Novelty and salience -- C Implementation -- D Drive decomposition -- References -- End-Effect Exploration Drive for Effective Motor Learning -- 1 Introduction -- 2 Method -- 2.1 A Probabilistic View to Motor Supervision -- 2.2 A Uniform Exploration Drive -- 2.3 Link with Variational Inference