![](https://dblp.uni-trier.de/img/logo.ua.320x120.png)
![](https://dblp.uni-trier.de/img/dropdown.dark.16x16.png)
![](https://dblp.uni-trier.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
![search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
default search action
IWAI 2022: Grenoble, France
- Christopher L. Buckley
, Daniela Cialfi
, Pablo Lanillos
, Maxwell Ramstead, Noor Sajid, Hideaki Shimazaki
, Tim Verbelen
:
Active Inference - Third International Workshop, IWAI 2022, Grenoble, France, September 19, 2022, Revised Selected Papers. Communications in Computer and Information Science 1721, Springer 2023, ISBN 978-3-031-28718-3 - Paul F. Kinghorn, Beren Millidge, Christopher L. Buckley:
Preventing Deterioration of Classification Accuracy in Predictive Coding Networks. 1-15 - Martin Biehl
, Nathaniel Virgo
:
Interpreting Systems as Solving POMDPs: A Step Towards a Formal Understanding of Agency. 16-31 - Stefano Ferraro, Toon Van de Maele, Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt:
Disentangling Shape and Pose for Object-Centric Deep Active Inference Models. 32-49 - Ruben S. van Bergen, Pablo Lanillos:
Object-Based Active Inference. 50-64 - Miguel Aguilera, Ángel Poc-López
, Conor Heins, Christopher L. Buckley:
Knitting a Markov Blanket is Hard When You are Out-of-Equilibrium: Two Examples in Canonical Nonequilibrium Models. 65-74 - Conor Heins, Brennan Klein, Daphne Demekas, Miguel Aguilera, Christopher L. Buckley:
Spin Glass Systems as Collective Active Inference. 75-98 - Mahault Albarracin
, Riddhi J. Pitliya, Maxwell J. D. Ramstead, Jeffrey Yoshimi:
Mapping Husserlian Phenomenology onto Active Inference. 99-111 - Jonathan Bauermeister, Pablo Lanillos:
The Role of Valence and Meta-awareness in Mirror Self-recognition Using Hierarchical Active Inference. 112-129 - Ran Wei, Alfredo García, Anthony D. McDonald
, Gustav Markkula, Johan Engström, Isaac Supeene, Matthew O'Kelly:
World Model Learning from Demonstrations with Active Inference: Application to Driving Behavior. 130-142 - Jakub Smékal
, Arhan Choudhury
, Amit Kumar Singh
, Shady El Damaty
, Daniel Ari Friedman
:
Active Blockference: cadCAD with Active Inference for Cognitive Systems Modeling. 143-150 - Beren Millidge, Christopher L. Buckley:
Active Inference Successor Representations. 151-161 - Justus F. Hübotter
, Serge Thill
, Marcel van Gerven
, Pablo Lanillos
:
Learning Policies for Continuous Control via Transition Models. 162-178 - Erica Santaguida, Massimo Bergamasco:
Attachment Theory in an Active Inference Framework: How Does Our Inner Model Take Shape? 179-191 - Alex B. Kiefer, Beren Millidge, Alexander Tschantz, Christopher L. Buckley:
Capsule Networks as Generative Models. 192-209 - Daria de Tinguy, Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt:
Home Run: Finding Your Way Home by Imagining Trajectories. 210-221 - Natalie Kastel, Guillaume Dumas
:
A Novel Model for Novelty: Modeling the Emergence of Innovation from Cumulative Culture. 222-234 - Dhanaraaj Raghuveer, Dominik Endres:
Active Inference and Psychology of Expectations: A Study of Formalizing ViolEx. 235-250 - Adam Safron
:
AIXI, FEP-AI, and Integrated World Models: Towards a Unified Understanding of Intelligence and Consciousness. 251-273 - Matteo Priorelli
, Ivilin Peev Stoianov
:
Intention Modulation for Multi-step Tasks in Continuous Time Active Inference. 274-284 - Samuel T. Wauthier
, Bram Vanhecke
, Tim Verbelen, Bart Dhoedt:
Learning Generative Models for Active Inference Using Tensor Networks. 285-297 - Dalton A. R. Sakthivadivel
:
A Worked Example of the Bayesian Mechanics of Classical Objects. 298-318 - Magnus T. Koudahl, Christopher L. Buckley, Bert de Vries:
A Message Passing Perspective on Planning Under Active Inference. 319-327 - Alex B. Kiefer, Mahault Albarracin
:
Efficient Search of Active Inference Policy Spaces Using k-Means. 328-342 - Adam Safron
, Zahra Sheikhbahaee, Nick J. Hay, Jeff Orchard, Jesse Hoey:
Value Cores for Inner and Outer Alignment: Simulating Personality Formation via Iterated Policy Selection and Preference Learning with Self-World Modeling Active Inference Agents. 343-354 - Eli Sennesh
, Jordan Theriault
, Jan-Willem van de Meent
, Lisa Feldman Barrett
, Karen S. Quigley
:
Deriving Time-Averaged Active Inference from Control Principles. 355-370
![](https://dblp.uni-trier.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.