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codetiger 1 days ago [-]
15yrs back I participated in "Google Ants AI Challenge 2011", an ai programming competition, hosted by the University of Waterloo, and I ranked #127 (#1 in my country). The competition gave me a huge learning oppurtunity where developers across the world came to a forum and discussed various techniques.
Now, I've built a similar platform to bring back the fun of building a small neural network that can play the game well. Neural Network optimization seems to be much more fun.
Plz share your feedback to improve the platform and add more games.
nickledave 4 hours ago [-]
Nice work, the new site looks great.
Can you give more background on the Ants game?
I didn't find it on the current site or the older one.
Was the game inspired by anything like agent-based simulations?
Would be cool if each ant itself could be an agent
codetiger 2 hours ago [-]
Unfortunately the competition site is mostly down and couldn't find much about the old competition other than the participants blog articles. Do a search on "Google Ants AI Challenge - post mortem", and you get a lot of articles around the game.
Thanks for sharing the research. I tried implementing a per Ant decision making model, but gave up as the training time was much longer compared to the current baseline. I think I should rethink the idea.
AnotherGoodName 16 hours ago [-]
Nice. I was 72nd. Working in AI research today and still making ai for games as a hobby (tfmbot.com is an ai i’m working on for my favourite board game terraforming mars).
codetiger 15 hours ago [-]
Thanks for sharing. I remember #1 xathis had a score, big leap ahead of others. The difference in techniques in top 100 was almost the same.
atmanactive 22 hours ago [-]
I remember a game on Steam called Tiny Brains, great couch co-op.
Muthaalagan 5 hours ago [-]
From competing with the world to building a place for the world to compete—what a full-circle moment. Love the challenge: how much strategy can a tiny neural network learn? Excited to see what people build.
WanderZil 2 hours ago [-]
This reminds me of John Conway's Game of Life. I wonder what surprises we could get by combining Game of Life with neural networks
euroderf 3 hours ago [-]
Let's play real stuff.
"Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks"
Man, I remember doing this is 2011 as well. Everything some kind of hand-coded strategy. I enjoyed it a lot.
Muthaalagan 5 hours ago [-]
Interesting—how small can a neural network get and still make good strategic decisions? Curious whether these models can adapt to unfamiliar opponents.
adityamishra241 11 hours ago [-]
This looks fun. How do you evaluate the networks — is it purely based on game performance, or are there other metrics like size and inference speed too?
codetiger 8 hours ago [-]
Glad you like it. The evaluation is based purely on game performance. However each weight class is evaluated separately. Nano, micro, mini, small, large and open class.
codetiger 8 hours ago [-]
When you submit a model it participates on both its weight class and the open class
willmarch 14 hours ago [-]
Pretty neat! I'm considering entering some models. How long will you be running these competitions?
codetiger 14 hours ago [-]
The current season is a public beta and ends by end of the month. After that am considering 3 month seasons.
willmarch 8 hours ago [-]
Signed up and submitted a test model. Now the real training begins!
DylanMerigaud 10 hours ago [-]
Great idea to focus on small, efficient neural networks.
Qworg 13 hours ago [-]
Reminds me of MechMania at UIUC - exciting!
vova_hn2 5 hours ago [-]
> Your class is measured, not chosen
> model and manifest bytes together pick the class
What?
How hard is it to write something like "your weight class is determined by the total size of the model and manifest" (if I understood it correctly).
Current version both sounds very AI-sloppy and is ambiguous.
Not all sloppy writing is AI. Quite a few humans also write ambiguously.
adityamishra241 24 hours ago [-]
This looks fun. How small are the networks you're aiming for?
sitzkrieg 18 hours ago [-]
the network size brackets are in TFA:
nano up to 16 KiB
micro up to 128 KiB
mini up to 1 MiB
small up to 8 MiB
large up to 64 MiB
codetiger 14 hours ago [-]
Each season has a different weight size restrictions. Currently open season is for a full production test.
cookiengineer 11 hours ago [-]
OMG!
Just yesterday I published my reworked GoNEAT library that implements HyperNEAT combined with phased search and backpropagation [1].
But it's kind of impossible to enter for me because of the hard pytorch requirements :( would love to see the project as a gym, so that you can run your own ANN design algorithm.
I get that most data science students still use python, but the evolutionary world is kinda in C++ and other native languages.
Where do you see a hard requirement? I have added support for ONNX model upload for now and would love to extend support for other formats. How you build the model is totally upto you. I don’t check anything other than format and inference time and model size.
codetiger 8 hours ago [-]
Saw your repo and understood you question better. The requirement are now limiting Neural Networks only, not a direct algorithm implementation
lostdog 16 hours ago [-]
Cool idea!
It would help to delete all the text on the page, and write it without AI.
For example, "model and manifest bytes together pick the class; every version also plays on Open"
codetiger 15 hours ago [-]
Thanks for the feedback. I’ll take that as top priority.
Now, I've built a similar platform to bring back the fun of building a small neural network that can play the game well. Neural Network optimization seems to be much more fun.
Plz share your feedback to improve the platform and add more games.
Can you give more background on the Ants game?
I didn't find it on the current site or the older one.
Was the game inspired by anything like agent-based simulations?
I'm not super interested in what the tech industry is calling "agentic" AI, but I am interested in collective intelligence, see David Ha's work in this area: - https://journals.sagepub.com/doi/full/10.1177/26339137221114... - https://neurips.cc/virtual/2024/105817
Would be cool if each ant itself could be an agent
Thanks for sharing the research. I tried implementing a per Ant decision making model, but gave up as the training time was much longer compared to the current baseline. I think I should rethink the idea.
"Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks"
https://arxiv.org/pdf/2502.13918
> model and manifest bytes together pick the class
What?
How hard is it to write something like "your weight class is determined by the total size of the model and manifest" (if I understood it correctly).
Current version both sounds very AI-sloppy and is ambiguous.
The doc page [0] is even more painful to read.
[0] https://tinybrains.dev/docs/models/weight-classes.html
Just yesterday I published my reworked GoNEAT library that implements HyperNEAT combined with phased search and backpropagation [1].
But it's kind of impossible to enter for me because of the hard pytorch requirements :( would love to see the project as a gym, so that you can run your own ANN design algorithm.
I get that most data science students still use python, but the evolutionary world is kinda in C++ and other native languages.
Anyways, great project nonetheless.
[1] https://github.com/cookiengineer/goneat
It would help to delete all the text on the page, and write it without AI.
For example, "model and manifest bytes together pick the class; every version also plays on Open"
https://en.wikipedia.org/wiki/Core_War