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OverviewFull Product DetailsAuthor: Christopher L. Buckley , Daniela Cialfi , Pablo Lanillos , Maxwell RamsteadPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 1st ed. 2024 Volume: 1915 Weight: 0.462kg ISBN: 9783031479571ISBN 10: 3031479572 Pages: 290 Publication Date: 16 November 2023 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsActive Inference and Robotics.- Contextual Qualitative Deterministic Models for Self-Learning Embodied Agents.- Dynamical Perception-Action Loop Formation with Developmental Embodiment for Hierarchical Active Inference.- Decision-making and Control.- Towards Metacognitive Robot Decision Making for Tool Selection.- Understanding Tool Discovery and Tool Innovation Using Active Inference.- Efficient Motor Learning Through Action-perception Cycles in Deep Kinematic Inference.- Active Inference and Psychology.- Towards Understanding Persons and their Personalities with Cybernetic Big 5 Theory and the Free Energy Principle and Active Inference (FEP-AI) Framework.- On Embedded Normativity - An Active Inference Account of Agency Beyond Flesh.- A Model of Agential Learning Using Active Inference.- From Theory to Implementation.- Designing Explainable Artificial Intelligence with Active Inference: A Framework for Transparent Introspection and Decision-making.- An Analytical Model of Active Inference in the Iterated Prisoner’s Dilemma.- Toward Design of Synthetic Active Inference Agents by Mere Mortals.- Learning Representations for Active Inference.- Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory.- Integrating Cognitive Map Learning and Active Inference for Planning in Ambiguous Environments.- Relative Representations for Cognitive Graphs.- Theory of Learning and Inference.- Active Inference in Hebbian Learning Networks.- Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks.- Probabilistic Majorization of Partially Observable Markov Decision Processes.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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