E-Book, Englisch, Band 1915, 290 Seiten, eBook
Buckley / Cialfi / Lanillos Active Inference
1. Auflage 2024
ISBN: 978-3-031-47958-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
4th International Workshop, IWAI 2023, Ghent, Belgium, September 13–15, 2023, Revised Selected Papers
E-Book, Englisch, Band 1915, 290 Seiten, eBook
Reihe: Communications in Computer and Information Science
ISBN: 978-3-031-47958-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Active 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.