> even markov chains can lead to behavior that looks this way. What a fun project, thank you for sharing it! re: > Once we created the creatures, we set up t

> even markov chains can lead to behavior that looks this way. What a fun project, thank you for sharing it! re: > Once we created the creatures, we set up their odors, which enable our creatures to smell them. When we have odors and creatures all we then have to do is build a the control components This sounds similar to the "collaborative diffusion" approach to pathfinding [1]. > However, it is possible to view the code on the github page. The link to https://github.com/lettergram/pleuro https://github.com/lettergram/pleuro does not work. Do you still have a copy of the code somewhere? I'm curious to learn more about how you modelled the control loops with Markov chains. Was the rough idea that there are states "forage", "eat", "protect", and then probability of transitions between states depends upon the simulated creature's current state & sensor information about the environment? edit: aha, i can see there's a 2018 publication by Gillette et al "Implementing Goal-Directed Foraging Decisions of a Simpler Nervous System in Simulation" [2] that has corresponding open-sourced cyberslug code [3] [1] Repenning 2006 "Collaborative diffusion: programming antiobjects" https://home.cs.colorado.edu/~ralex/papers/PDF/OOPSLA06antiobjects.pdf https://home.cs.colorado.edu/~ralex/papers/PDF/OOPSLA06antio... [2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830682/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830682/ [3] https://github.com/derekca/cyberslug https://github.com/derekca/cyberslug not the same codebase, but same advisor, so the code may be some loose relative. alas, the linked codebase seems rather "academic research quality", in the sense that there there's 5000 lines of netlogo cyberslug code and there don't appear to be any automated tests.