119 citations · 323 across the 24 of their papers we have counts for
6 papers · 1 filter
Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification
Karim Zaghw, Andrew Pashea, Marc Pritsch +3
Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficul…
Toward Universal and Interpretable World Models for Open-ended Learning Agents
Lancelot Da Costa
We introduce a generic, compositional and interpretable class of generative world models that supports open-ended learning agents. This is a sparse class of Bayesian networks capab…
Possible Principles for Aligned Structure Learning Agents
Lancelot Da Costa, Tomáš Gavenčiak, David Hyland +5
This paper offers a roadmap for the development of scalable aligned artificial intelligence (AI) from first principle descriptions of natural intelligence. In brief, a possible pat…
Active Inference as a Model of Agency
Lancelot Da Costa, Samuel Tenka, Dominic Zhao +1
Is there a canonical way to think of agency beyond reward maximisation? In this paper, we show that any type of behaviour complying with physically sound assumptions about how macr…
Branching Time Active Inference: the theory and its generality
Théophile Champion, Lancelot Da Costa, Howard Bowman +1
Over the last 10 to 15 years, active inference has helped to explain various brain mechanisms from habit formation to dopaminergic discharge and even modelling curiosity. However,…
Reward Maximisation through Discrete Active Inference
Lancelot Da Costa, Noor Sajid, Thomas Parr +2
Active inference is a probabilistic framework for modelling the behaviour of biological and artificial agents, which derives from the principle of minimising free energy. In recent…