231 citations · 607 across the 16 of their papers we have counts for
17 papers
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…
Supervised structure learning
Karl J. Friston, Lancelot Da Costa, Alexander Tschantz +10
This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a s…
Hierarchical generative modelling for autonomous robots
Kai Yuan, Noor Sajid, Karl Friston +1
Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundame…
On efficient computation in active inference
Aswin Paul, Noor Sajid, Lancelot Da Costa +1
Despite being recognized as neurobiologically plausible, active inference faces difficulties when employed to simulate intelligent behaviour in complex environments due to its comp…
Bistable perception, precision and neuromodulation
Filip Novicky, Thomas Parr, Karl Friston +2
Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bist…
Modelling non-reinforced preferences using selective attention
Noor Sajid, Panagiotis Tigas, Zafeirios Fountas +3
How can artificial agents learn non-reinforced preferences to continuously adapt their behaviour to a changing environment? We decompose this question into two challenges: () en…
Reclaiming saliency: rhythmic precision-modulated action and perception
Ajith Anil Meera, Filip Novicky, Thomas Parr +3
Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual informatio…
The free energy principle made simpler but not too simple
Karl Friston, Lancelot Da Costa, Noor Sajid +4
This paper provides a concise description of the free energy principle, starting from a formulation of random dynamical systems in terms of a Langevin equation and ending with a Ba…
Active inference, Bayesian optimal design, and expected utility
Noor Sajid, Lancelot Da Costa, Thomas Parr +1
Active inference, a corollary of the free energy principle, is a formal way of describing the behavior of certain kinds of random dynamical systems that have the appearance of sent…
Active Inference for Stochastic Control
Aswin Paul, Noor Sajid, Manoj Gopalkrishnan +1
Active inference has emerged as an alternative approach to control problems given its intuitive (probabilistic) formalism. However, despite its theoretical utility, computational i…
Exploration and preference satisfaction trade-off in reward-free learning
Noor Sajid, Panagiotis Tigas, Alexey Zakharov +2
Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of beh…
Bayesian brains and the Rényi divergence
Noor Sajid, Francesco Faccio, Lancelot Da Costa +3
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters. This provides a formal explanation for why indiv…
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…
Deep active inference agents using Monte-Carlo methods
Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano +1
Active inference is a Bayesian framework for understanding biological intelligence. The underlying theory brings together perception and action under one single imperative: minimiz…
Active inference on discrete state-spaces: a synthesis
Lancelot Da Costa, Thomas Parr, Noor Sajid +3
Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associ…
Active inference: demystified and compared
Noor Sajid, Philip J. Ball, Thomas Parr +1
Active inference is a first principle account of how autonomous agents operate in dynamic, non-stationary environments. This problem is also considered in reinforcement learning (R…
Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI
Yusuf H. Roohani, Noor Sajid, Pranava Madhyastha +2
One third of stroke survivors have language difficulties. Emerging evidence suggests that their likelihood of recovery depends mainly on the damage to language centers. Thus previo…