Agency with structured latent state-spaces: a model for computational phenomenology.

Hello and welcome. It's September 15th, 2023, and we're at the Active Inference Institute in ACTINF Guest Stream number 56.1. Today we have Gregory Sargent-Petri, and we'll be hearing a talk followed by a discussion on Geometry of World Model Influences Behaviors. First, there'll be a presentation, and then any comments that people have in the live chat or any other questions, it'll be great to talk. So thank you a lot for coming. Really looking forward to this. So please, to you. Thank you very much, Daniel, for the invitation. First, I'm really very happy to be able to present this work here. So it's a work around some models of consciousness, at least some computational models of consciousness, some part of consciousness, and how it can help to generate different kind of behaviors, especially when we think about, let's say, artificial agents. So it's a work in collaboration with David Rudroff, Kenneth Williford, Daniel Benquin. In fact, it's not just one work. It's a collection of works that have been on for like already around 10 years. So if you want to know more about like this group and the work that we're doing, you can go on the page on my personal web page, and there's a link to PCM.html. And there's a summary of all the work we've been doing and some summary of the work we've been doing and also of some articles that can be relevant on this subject. So today, we'll focus more particularly on two articles. So one that is accepted and will appear very soon, the other one which we submit, which give the formulation of, let's say, autonomous agents, or like, let's say, mathematical formulation of agents that have a world model that is structured geometrically in such a way that this world model captures some features of consciousness. So the first article is, let's say, the review of the experimental results that we have, and the like most recent formalism that we have on our work, which is the literature on POMD for terrible monitoring process, which is optimal control, stochastic optimal control. And we just try to tweak a bit this formalism to introduce the ideas with how you can include inside of the world model of the agent, some ideas on consciousness, and still continue to have algorithms to do inference to find optimal policies and everything. And the second article focuses more particularly on how changing the world model of the agents can change its behavior, in particular, the relationship, the geometry of the world model, the way that it perceives its environment, and the way it acts with respect to for agent strategy. So the way that it looks for something, so it will change its behavior in looking for something. So I will start by presenting a bit what are all these terms, so world model, what you need it, what it is, what is a foraging strategy, what it is to, how you, how can you define an agent that is looking for a certain object, in particular, what is an exploration based on curiosity, or let's say, more technically epistemic value. So let's consider the setting where you have, let's say, a real world, so the space that's around us, 3D space, you do a setup where you have an agent, which is, let's say, a solid, it has a solid frame. So it has a center, three axis, what is in front, what is on its side, what's above, above it. And it's looking for an object O, which is itself a solid inside of R3. And all the configuration of the agents and the objects, they are defined by, let's say, a reference frame that is external and that they find that characterizes completely their configuration inside of this world. Now the agent A, what it wants to do is to find for the object O, but it doesn't, it can only have like some noisy observation of where the object O is. So to be able to find O, it needs to have some a priori on where O should be, to be able to plan the consequences of its actions with respect to where O will be once it has moved and to update and to be able to plan what the, how the observation…