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Found 6 papers, 2 papers with code
Interpreting systems as solving POMDPs: a step towards a formal understanding of agency
To do this we make use of the existing theory of partially observable Markov processes (POMDPs): we say that a system can be interpreted as a solution to a POMDP if it not only admits an interpretation map describing its beliefs about the hidden state of a POMDP but also takes actions that are optimal according to its belief state.
Interpreting Dynamical Systems as Bayesian Reasoners
A central concept in active inference is that the internal states of a physical system parametrise probability measures over states of the external world.
On the potential for open-endedness in neural networks
In this paper, we hope to bridge that gap by reviewing common barriers to open-endedness in the evolution-inspired approach and how they are dealt with in the evolutionary case - collapse of diversity, saturation of complexity, and failure to form new kinds of individuality.
Being curious about the answers to questions: novelty search with learned attention
We investigate the use of attentional neural network layers in order to learn a `behavior characterization' which can be used to drive novelty search and curiosity-based policies.
Permutation-equivariant neural networks applied to dynamics prediction
The introduction of convolutional layers greatly advanced the performance of neural networks on image tasks due to innately capturing a way of encoding and learning translation-invariant operations, matching one of the underlying symmetries of the image domain.
Motility at the origin of life: Its characterization and a model
On this view, self-movement, adaptive behavior and morphological changes could have already been present at the origin of life.
