69 citations · 106 across the 15 of their papers we have counts for
11 papers · 1 filter
Interpreting systems as solving POMDPs: a step towards a formal understanding of agency
Martin Biehl, Nathaniel Virgo
Under what circumstances can a system be said to have beliefs and goals, and how do such agency-related features relate to its physical state? Recent work has proposed a notion of…
Interpreting Dynamical Systems as Bayesian Reasoners
Nathaniel Virgo, Martin Biehl, Simon McGregor
A central concept in active inference is that the internal states of a physical system parametrise probability measures over states of the external world. These can be seen as an a…
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments
Francesco Massari, Martin Biehl, Lisa Meeden +1
Reinforcement Learning (RL) is known to be often unsuccessful in environments with sparse extrinsic rewards. A possible countermeasure is to endow RL agents with an intrinsic rewar…
Geometry of Friston's active inference
Martin Biehl
We reconstruct Karl Friston's active inference and give a geometrical interpretation of it.
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
Martin Biehl, Christian Guckelsberger, Christoph Salge +2
Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologicall…
Being curious about the answers to questions: novelty search with learned attention
Nicholas Guttenberg, Martin Biehl, Nathaniel Virgo +1
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.…
Learning body-affordances to simplify action spaces
Nicholas Guttenberg, Martin Biehl, Ryota Kanai
Controlling embodied agents with many actuated degrees of freedom is a challenging task. We propose a method that can discover and interpolate between context dependent high-level…
Action and perception for spatiotemporal patterns
Martin Biehl, Daniel Polani
This is a contribution to the formalization of the concept of agents in multivariate Markov chains. Agents are commonly defined as entities that act, perceive, and are goal-directe…
Formal approaches to a definition of agents
Martin Biehl
This thesis contributes to the formalisation of the notion of an agent within the class of finite multivariate Markov chains. Agents are seen as entities that act, perceive, and ar…
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks
Nicholas Guttenberg, Martin Biehl, Ryota Kanai
We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals…
Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems
Martin Biehl, Takashi Ikegami, Daniel Polani
We present some arguments why existing methods for representing agents fall short in applications crucial to artificial life. Using a thought experiment involving a fictitious dyna…