Buckley / Cialfi / Lanillos Active Inference

5th International Workshop, IWAI 2024, Oxford, UK, September 9–11, 2024, Revised Selected Papers
Erscheinungsjahr 2024
ISBN: 978-3-031-77138-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2193, 275 Seiten, eBook

Reihe: Communications in Computer and Information Science

This book constitutes the revised selected papers of the 5th International Workshop on Active Inference, IWAI 2024, held in Oxford, UK, during September 9–11, 2024.The 17 full papers included in this book were carefully reviewed and selected from 54 submissions. They were organized in topical sections as follows: Modeling Behaviour and Emotions; Hybrid Continuous-discrete Systems; Structure Learning; Multi-agent Systems; Epistemic Sampling; Robot Control; and Sustainability and Contextuality.

Zielgruppe

Research

Autoren/Hrsg.

Buckley, Christopher L.

Cialfi, Daniela

Lanillos, Pablo

Pitliya, Riddhi J.

Sajid, Noor

Shimazaki, Hideaki

Verbelen, Tim

Wisse, Martijn

Inhaltsverzeichnis

.- Modeling behaviour and emotions.

.- Towards Interaction Design with Active Inference: A Case Study on Noisy Ordinal Selection.

.- Modelling Agency Perception in Depression Using Active Inference: A Multi-Agent Behavioural Study.

.- Free Energy in a Circumplex Model of Emotions.

.- Hybrid continuous-discrete systems.

.- Learning in Hybrid Active Inference Models.

.- Learning and embodied decisions in active inference.

.- Structure learning.

.- Online Structure Learning with Dirichlet Processes through Message Passing.

.- Exploring and Learning Structure: Active Inference Approach in Navigational Agents.

.- Multi-agent systems.

.- Belief sharing: a blessing or a curse.

.- Coupled autoregressive active inference agents for control of multi-joint dynamical systems.

.- Reactive Environments for Active Inference Agents with RxEnvironments.

.- Epistemic sampling.

.- Selection of Exploratory or Goal-Directed Behavior by a Physical Robot Implementing Deep Active Inference.

.- Epistemic Value Anticipation into the Deep Active Inference Model.

.- Robot control.

.- Planning to avoid ambiguous states through Gaussian approximations to non-linear sensors in active inference agents.

.- Message Passing-based Bayesian Control of a Cart-Pole System.

.- Reducing Intuitive-Physics Prediction Error through Playing.

.- Sustainability and contextuality.

.- Modeling Sustainability under Active Inference through resource management.

.- Contextuality, Cognitive engagement, and Active Inference.