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The free energy principle is a mathematical theory of the behaviour of self-organising systems that originally gained prominence as a unified model of the brain. Since then, the theory has been applied to a plethora of biological phenomena, extending from single-celled and multicellular organisms through to niche construction and human culture, and even the emergence of life itself. The free energy principle tells us that perception and action operate synergistically to minimize an organism’s exposure to surprising biological states, which are more likely to lead to decay. A key corollary of this hypothesis is active inference—the idea that all behavior involves the selective sampling of sensory data so that we experience what we expect to (in order to avoid surprises). Simply put, we act upon the world to fulfill our expectations. It is now widely recognized that the implications of the free energy principle for our understanding of the human mind and behavior are far-reaching and profound. To date, however, its capacity to extend beyond our brain—to more generally explain living and other complex adaptive systems—has only just begun to be explored. The aim of this collection is to showcase the breadth of the free energy principle as a unified theory of complex adaptive systems—conscious, social, living, or not.

Information technology industries --- Computer science --- message passing --- metabolism --- Bayesian --- stochastic --- non-equilibrium --- master equations --- cancer niches --- free energy --- Kikuchi approximations --- apoptosis --- metastasis --- cluster variation method --- Free Energy Principle --- active inference --- Bayesian brain --- generative models --- cybernetics --- embodiment --- enactivism --- cognitivism --- representations --- consciousness --- free will --- mental causation --- cognitive-affective development --- emotions --- feelings --- readiness potentials --- intentionality --- agency --- intelligence --- collective intelligence --- free energy principle --- agent-based model --- complex adaptive systems --- multiscale systems --- computational model --- uncertainty --- POMDP --- emotion --- affect control theory --- sociology --- permutation entropy --- disorder --- stress --- allostatic (hub) overload --- cascading failure --- disease --- hierarchical control systems --- critical slowing down --- model-based control --- adaptive robots --- generative model --- Bayesian inference --- filtering --- neurotechnology --- n/a

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The free energy principle is a mathematical theory of the behaviour of self-organising systems that originally gained prominence as a unified model of the brain. Since then, the theory has been applied to a plethora of biological phenomena, extending from single-celled and multicellular organisms through to niche construction and human culture, and even the emergence of life itself. The free energy principle tells us that perception and action operate synergistically to minimize an organism’s exposure to surprising biological states, which are more likely to lead to decay. A key corollary of this hypothesis is active inference—the idea that all behavior involves the selective sampling of sensory data so that we experience what we expect to (in order to avoid surprises). Simply put, we act upon the world to fulfill our expectations. It is now widely recognized that the implications of the free energy principle for our understanding of the human mind and behavior are far-reaching and profound. To date, however, its capacity to extend beyond our brain—to more generally explain living and other complex adaptive systems—has only just begun to be explored. The aim of this collection is to showcase the breadth of the free energy principle as a unified theory of complex adaptive systems—conscious, social, living, or not.

Information technology industries --- Computer science --- message passing --- metabolism --- Bayesian --- stochastic --- non-equilibrium --- master equations --- cancer niches --- free energy --- Kikuchi approximations --- apoptosis --- metastasis --- cluster variation method --- Free Energy Principle --- active inference --- Bayesian brain --- generative models --- cybernetics --- embodiment --- enactivism --- cognitivism --- representations --- consciousness --- free will --- mental causation --- cognitive-affective development --- emotions --- feelings --- readiness potentials --- intentionality --- agency --- intelligence --- collective intelligence --- free energy principle --- agent-based model --- complex adaptive systems --- multiscale systems --- computational model --- uncertainty --- POMDP --- emotion --- affect control theory --- sociology --- permutation entropy --- disorder --- stress --- allostatic (hub) overload --- cascading failure --- disease --- hierarchical control systems --- critical slowing down --- model-based control --- adaptive robots --- generative model --- Bayesian inference --- filtering --- neurotechnology --- message passing --- metabolism --- Bayesian --- stochastic --- non-equilibrium --- master equations --- cancer niches --- free energy --- Kikuchi approximations --- apoptosis --- metastasis --- cluster variation method --- Free Energy Principle --- active inference --- Bayesian brain --- generative models --- cybernetics --- embodiment --- enactivism --- cognitivism --- representations --- consciousness --- free will --- mental causation --- cognitive-affective development --- emotions --- feelings --- readiness potentials --- intentionality --- agency --- intelligence --- collective intelligence --- free energy principle --- agent-based model --- complex adaptive systems --- multiscale systems --- computational model --- uncertainty --- POMDP --- emotion --- affect control theory --- sociology --- permutation entropy --- disorder --- stress --- allostatic (hub) overload --- cascading failure --- disease --- hierarchical control systems --- critical slowing down --- model-based control --- adaptive robots --- generative model --- Bayesian inference --- filtering --- neurotechnology

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This volume constitutes the papers of the 4th International Workshop on Active Inference, IWAI 2023, held in Ghent, Belgium on September 2023. The 17 full papers included in this book were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: active inference and robotics; decision-making and control; active inference and psychology; from theory to implementation; learning representations for active inference; and theory of learning and inference.

Artificial intelligence. --- Computer science --- Mathematical statistics. --- Computer networks. --- Application software. --- Computers, Special purpose. --- Software engineering. --- Artificial Intelligence. --- Probability and Statistics in Computer Science. --- Computer Communication Networks. --- Computer and Information Systems Applications. --- Special Purpose and Application-Based Systems. --- Software Engineering. --- Mathematics. --- Intel·ligència artificial --- Inferència

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Social sciences (general) --- Computer assisted instruction --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- informatica --- sociale wetenschappen --- computerondersteund onderwijs --- wiskunde --- software engineering --- KI (kunstmatige intelligentie)

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This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops: Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021) Workshop on Parallel, Distributed and Federated Learning (PDFL 2021) Workshop on Graph Embedding and Mining (GEM 2021) Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021) Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021) Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021) Workshop on Bias and Fairness in AI (BIAS 2021) Workshop on Workshop on Active Inference (IWAI 2021) Workshop on Machine Learning for Cybersecurity (MLCS 2021) Workshop on Machine Learning in Software Engineering (MLiSE 2021) Workshop on MIning Data for financial applications (MIDAS 2021) Sixth Workshop on Data Science for Social Good (SoGood 2021) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021) Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021).

Social sciences (general) --- Computer assisted instruction --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- informatica --- sociale wetenschappen --- computerondersteund onderwijs --- wiskunde --- software engineering --- KI (kunstmatige intelligentie)