Active Inferants: An Active Inference Framework for Ant Colony Behavior

[Music] hello everybody welcome to the active inference lab this is live stream number 29.0 on september 14th is it september 14th not anymore september 17 17. at least where we're at on september 17 2021 welcome to the active inference lab we are a participatory online lab that is communicating learning and practicing applied active inference you can find us at the links here on this slide this is recorded in an archived live stream so please provide us with feedback so that we can improve our work all backgrounds and perspectives are welcome and we'll be following good video etiquette for live streams at the short link on this slide you can see the past present and future live streams and just to call your attention to the tabs on the bottom the first tab are our regular tuesday live streams so for these ones we do the zero contextualizing video that's what you're watching now and then we have a dot one and a dot two where on two successive weeks we try to have the authors try to have a lot of different people on a panel just talking about the paper the guest stream is sort of a wild card slash anything goes we have a lot of different topics model streams reflect machine learning different kinds of modeling and walkthroughs of code and the math stream is for talking about math and different formal frameworks so if you are interested in co-organizing or presenting for any of these different kinds of streams then just get in touch with us today in active stream 29 we're going to be having the goal of learning and discussing this paper active inference and active inference framework for ant colony behavior by freedman chance ramstead fristen and constant and just like all of the other.0 videos this is just an introduction to some of the ideas it's not a review or a final word and it doesn't change too much even when the first or any author is on the stream we're gonna go over the keywords as well as the aims and claims the abstract all the usual things that we cover in the dot zeros and in the coming weeks we're gonna be discussing this paper so everybody is welcome to join from whatever area they're excited about the paper from just get in touch with us so we can begin with a little introductions and warm-ups um blue how about you can go first and then we'll pass it to the first author so i'm blue knight an independent research consultant from new mexico and i'll pass to daniel friedman the first author of this paper thanks blue so i'm daniel i'm a postdoc researcher in california and yeah usually we have everybody except for the first author introduce themselves and then the first author uh steps in and gives a little background on the paper so it's fun it's the first time that we've discussed one of my papers on the stream and uh we'll be talking about a bunch of different aspects of it and it's awesome to have you here blue with a lot of expertise in collective behavior and a bunch of other areas the big question that motivated not just this live stream but this whole line of research in this paper is assuming that each agent is a quote from the paper assuming that each agent nestmate has only limited access to incoming information how can a group of active inference agents solve complex group foraging problems so in the case of ants that local information is going to be the pheromone density the chemicals that it can perceive its local umvelt but in the case of information foraging it's what we see or it's our perspective or the people who we can communicate to and so this is a topic that's come up in many different guises which is again when you have a multi-agent system using active inference to model each agents how do we think about the way that the perceptions and cognition and action of each individual active inference agents how do those compose or interact so that the group can have adaptive or interesting behavior the big question of the paper and also again just a big open question hardly one that gets answered…