i've wondered what kind of pressures could exist for a mound-building simulation. maybe it's cold outside and they lose health if they are not near a shelter block

This is quickly becoming one of my favorite channels - totally fascinating topics

I love this so much. I made a copycat program for myself to mess around with. I added a predator object that tends toward the nearest agent. When the agents shout the distance they think is to the nearest predator, instead of the others turning toward them, they turn exactly away from them if that distance is within a certain threshold. Makes for some interesting behavior-scattering, followed by reorganization. I'm going to gear mine toward a natural selection simulation. Queens will have genetic information (possibly neural networks) that dictate what they try to do with each timestep (heal, create a certain kind of agent, etc.). Not exactly sure how I want to create the selective pressures for that yet though. I think I'm going to let them make a pseudoqueen that tries to get far away from her and then, when she eventually does, she establishes herself as a new queen. Expensive resource-wise to do so though, so she'll need careful planning. Excited to see what you do with this going forward!

Since I see no one trying to explain the termite algorithm, here's my take: I think one should think about "bar of grains" as pillars of a (locally) cone shaped building. I also think it is really important for this algorithm to take into account that: - different agents start at different times and in different locations. - many termites may be working simultaneously on the same pillar. Also, when talking about heights, they are always relative to the ones around the specific pillar. -- Algorithm -- The termites start working on their personal pillars [step 1] Since some termites started before, they will reach H1 ("height 1") before others [step 1 -> 2]. Those will continue to work on their pillar until they reach H2 ("height 2) [step 2 -> 3]. The others that started later will reach H1 and find out that there is a bigger pillar nearby, thus moving towards it while it has still not reached H2 [step 2 -> 3]. Once the pillar reaches H2, all termites are freed to move to a bigger pillar if it exists [step 3 -> 2], or to create support arcs [step 3]. While creating support arcs, there's still a lower pillar without an arc, go there and build it [step 3 -> 3]. Otherwise go to the "base height" of all pillars and start building another one. ---- So to my understanding, the termites will tend to gather more in the bigger pillars, forming radial structures similar to the forming paths shown in the video.

This is objectively the most underrated channel on yt

I am starting to wonder whether these ideas are written out and executed with a lot of artificial intelligence on the backend and minimal human interference. I feel like this is what that would look like. Deeply creative work lads.

I love the concept of this, especially the civilization type idea at the end of the video.

I find this really interesting. When you think about it, human body is also just cells communicating with each other. A single one of them doesn't have a solution for a problem, but as a whole they do.

You need to combine this with a genetic algorithm!

Да неужели это то, что я думаю? До чего же дошли речевые технологии!

Amazing logic on not walking straight, and the moving queen and new queens.. mind-blowing, thank u so much for thinking honestly

this brings a whole new meaning to "lightning bugs"

I like the contrast between how the ant screams in this version use an arcade shooting sound, while the Russian one used eldritch noises

computer simulated organisms and evolution is just so cool to me. I love this type of content!

This is unbelievably fascinating please continue this was a part two about the termite mound building algorithm. Also I would love it if you would post a video with just the algorithms running because they’re so interesting to watch

Can't help but feel this has a most prevalent use in mass organizing and surveillance via mobile device.

new queens be like: "Im spartacus!" "No im spartacus!"

Thank you so much for your all effort

My observation of swarms is when I am working on digging up a tree stump. Just before the stump is ready to come loose, waves of mosquitoes attack me, as if in defense of the stump. I can tell the stump is ready to move by the swarms intensity. It also happens when I burn the brush pile. I know the fire will stay lit when the mosquitoes form a cloud around my head. The correlation is maddeningly consistent. I am certain there is a causal connection to these events.

your videos are mad interesting please keep making them

i dont know how i stumbled onto this video but this was realy interesting to see, verry well explained and quite cool!

Air flow and temperature is what guides termite mound design

That's a good one.

pure gold here. great job

I love your videos, keep it up!

Reminds me of the work that was being done on the game Limit Theory at one point where the world was going to have an ever evolving economy system of npcs bringing goods around from world to world

screaming agents be like: *AAAAAAAAAAAAAAAAAAAAAAAAAAAAA*

another great video, I forgot I subscribed to your patreon so I was surprised when I saw the credits lol

This is brilliant!

Anyone else see the similarity to the formation of lightning when the paths are being formed? (around 6:00)

very interesting algorithm, i like the simple art style reminds me of an ant pheromone trail sim i wrote a while back

This was a great showcase of something I learned in a course on Multigrid Methods. Local updates are great for quickly smoothing out high frequency errors, however take much much longer to deal with the remaining low frequency errors afterwards. I wonder if doubling the shouting range after some time would be like sampling down to a coarser grid, making the errors more high frequency.

For your mound problem, the concept is simple enough. The morphology of mounds are based on the local environment, because ants/termites need: - a mound that has good heat transport or retention (thickness of the walls, geometry vs sunlight) - a mound that distributes pheromones properly (just humid and warm enough to let them either stick to surfaces or be carried through the corridors? not sure about this one) - a mound that serves the function of lung, to let air transport humidity and carbon dioxide out of the mound and import fresh air (porosity of the walls) - mounds that serve as an environment to cultivate fungal species that they eat (again, right light, temperature and humidity) Deciding factors in the morphology are therefore: - Abundance and average height of the flora around (shade, soil humidity, resources, etc) - Amount of precipitation and wind - Distance to the equator (how much sunlight they get, how extreme the day/night cycle) - Soil quality (how much the soil retains water, how decent it is as a building material, how much the soil can serve as a substrate for fungal species, how hot it can get) - The species of termite/ant involved. You can take any combination of temperature / humidity / carbondioxide versus fresh air / fungal substrate as "incentives" to create structures in both 2D and 3D, not necessarily in the shape of actual mounds, that fit the role of "base of operations" for the queen and home for the colony. In my version, I'm going to separate 'builder' agents from 'foragers', so that the builders can walk over and deposit structure on any pixel, but foragers have to go around walls and stuff. The most important excerpts from the paper linked below: "mechanisms can be roughly divided into two broad categories: the secretion of a templating odor at the nest that undergoes diffusion and advection globally throughout the mound, and the local deposition of a building pheromone in deposited soil pellets that promote further deposition nearby" "carbon dioxide gradients may be used as templates in nest construction" "[mounds] where convective currents dominate conduction, can grow in a focused direction due to the airflow transporting the odor in the direction of growth." “The growth of mounds is dominated by building processes at the surface and limited by molecular diffusion of pheromones through the mound wall." "As a mound grows, unless more termites are recruited into the building process, the volumetric density of termites decreases over time." "larger colonies will tend to produce a greater quantity of odor, and this high rate [sic] will result in a large mound radius [sic]." "the absolute size of termite mounds increases [taller] with the amplitude of temperature oscillations" (of the day/night temperature cycle) "external wind was found to play a subordinate role relative to the dominant thermal mechanism" "we have neglected here the effects of active transport of water by termites within the mound and the passive dynamics of evaporative cooling that will change the temperature profiles and thereby the mechanics of odor transport" www.pnas.org/doi/10.1073/pnas.1818759116

This is the best channel for life simulation ever! BTW, can you use the concepts used in the tree evolution to make them evolve "screaming" that is better?

So doug doug's twitch chat is a good example of that It is a swarm of maniacs that work together that try to complete a task together

All of your videos are great! It makes me motivated to to a project like tihis on my own

You should add some "resistance" in which the more agents near each other the more likely a few agents will not go with the crowd and look for other sources. You could also have mobility where more resources either slow down or speed up the cell(depending on if you see it as extra weight to carry or as some "knowledge"/"tool"/"strength"). You could also add "crime" where some cells can kill or steal from others. Obviously you can do all the things everyone else does in these types of simulations to make them more complex looking.

Amazing video.

love these types of videos

Huh, I did not get notified for the video.. anywho, great video as always, thank you for making so easily understandable content ^_^

I like that you used 3 resources just like most insects specifically ants need. Water, Sugar & protein. Also the concept of lifespans & new queens. I've got a little bit of experience with programming & after watching this I kinda wanna try my own hand at making a similar simulation.

Thank you for this new Video ❣❣❣

Awesome. Just awesome

Really cool demonstration. Looking to do similar projects in the future. Tangental thought. I wonder how this can be related to the market.

yessssssss that would be absolutely amazing

Very interesting!

He's back! Let's go!!!!

this vid was really fun to watch as i like bugs

Hey I haven't even watched the video yet, but you had me at "screaming insects"! Wicked cool title!

Nice! I like this.

Not just insect, this is probably how electrons form a thunder

Greate Stuff! wish there was a simple open-source for this for people to play around with

12:10 Original Queen is still alive on the right side.

Swarm intelligence is really interesting, ants on their own seems just like stupid animals but on a large scale whole colony looks intelligent

Cool. I had just subscribed.

Does the algorithm assume that each shout includes both the value and which location it relates to? So, an agent would effectively shouting "127 to A" as opposed to just "127"?

now we need a one where the agents will go to war between queens

I would love to see more screaming incects

the queen can only get one agent at a time, cause they block each other or slow each other down | if there are walls build, then they can move without restrictions and over each other

Fascinating! (Maybe I need a 'Spock raising a single eyebrow' emoji!)

Intresting.

The economic simulation idea reminded me of German-style boardgames. What if the unchosen resource piles also increase over time(or add efficiency with more resource per carried grain)? Thus agents also need to choose between close but inefficient resources vs distant but efficient

Nice

The pathways look more like the evolution of nerves which is probably the purpose of this video except real life nerves are more random.

I feel like this could be a useful analogy for particle physics

This is so cool! What program is this written in?? Like pygame or something.

he made 3 things, 1. ants 2. bees 3. humans in an online envoirement right now

I recreated this in processing, one problem I am having is once the agent numbers increase it slows down alot, I tried some optimization methods of my own to fix it, but I want to know how did you tackle that. Also when the agents bump into the food/hive and are supposed to turn around 180 degrees, in my case they seem to spread outwards in random directions, maybe it has to do with the angle they hit the object just before turning around? Any idea about that?

I wonder if there are phase transitions if you add noise! (And what the system on the edge of them looks like.)

Wow, fascinating! I'm curious about which language did you use to implement this simulation?

Great project! Strange narration 😂

Once the queen is full health for long enough, she start spitting out building block for the mound?

nice new vid

Ребят, официальный канал на русском написал в видео с название About the channel, что дал согласие на это. Кажется это не его канал, но кого то , кому он дал разрешение на это.

What if there is a wall in the direct path between food and queen allowing only to go sideways for a given length ? Will the agent be able to find a path ?

Such an amazing video. I am new to this so can anyone answer this. Is the explanation given above based on "PSO"?

I'd argue that it should be possible for Agents to consume other agents at chance, whilst agents in general can generate with a random "capacity," increased only by consuming others - to a limit dictated by how many are around. And, well, the queens... I wonder what happens if the queens vanished?

Great video. Bee hive yourself 😉

Great video! Would love some human narration, if that's at all possible in the future. The voice is a bit weird to me.

I wonder how the swam intelligence scales with both agent intelligence and the number of agents. Is there a phase transition at some number of agents, where after that they find path (albeit after a long time) but if you only have one agent less they will never make a path?

Hmmm... What if there are multiple points at equal distances? Like 6 points beeing placed at the same distance(3 blue and 3 orange)?

I’m curious how well did this perform, did it require a lot of optimisation? I tried programming this up in Python, spent a lot of time optimising it to try and get it to run well, but even after a lot of work minimising the run time and writing in quad trees, it kind of hits a hard limit around 800 insects dropping down to about 10 frames a second once the insects group up in lines between food and hive. What was everyone else’s experience with performance?

This should be used it video games. Like a kingdom or village builders, and the agents are people with job and need for resources.

🤔 "what kind of algorithm should be used for ants to build city?".. I think some ideas from "DNA algorithms" can be used. all of them can be described like "if conditions like A than doing B"

pog

Ants on their own aren't mindless automata.

4:38 can you record this scene while highligting the actual code that you used to program that checklist that each creature contained

Reminds me of the ant simulators.

huh, this is intresting

Building feels like it only makes sense if some resources are stationary.

Do you have a discord? Or a subreddit? I think it would be nice for people who are trying/have implementing these things to share their project with eachother.

Original video: kzfaq.info/get/bejne/hrqEfL1pqtPGdYU.html

Then it s an effect of a distribution of random walks with an action, in interaction...

I like how you've accidentally created electrons

if someone reads my comment , please answer my question , 1. which tool or programming language was used for this ; 2. how is this thing so damn efficient in terms of performance, there are 1000s of particle in the last one and it is so smooth , my programs generally start giving only 2-3 fps after reaching such strength, btw i use p5.js for creating simulations

No offense but when I play it on 1.25 speed you sound normal. Do you slow your audio down to get that play time?

What if you were to make the queens competitive, stealing resources, agents, and resources. What if the queens could evolve?

In what language do you perform all these simulations?

what happens if there are no borders on the edge of the map, would they would all get lost?

the video script seems just kinda off, but cool stuff