One possible way to do this would be to use some kind of neural network (maybe some CNN, MLP, RNN or even a Transformer ) to find the number of new rows that need to be added in order to find an extension. I would think the best would be an RNN since the rows are some kind of a sequence. Transformers could work as well to model longer range relationships between the rows using self-attention for high period ships.
Of course there are a few issues with this:
- the inference time of the neural network would be quite large (but maybe this can be solved using Knowledge Distillation to compress the neural network size or running the search on a GPU)
- it seems that the neural network would need to be re-trained for different widths and speeds and rules which is a bit troublesome (perhaps there would be a way to give the neural network information about the speeds, maybe some similar to this?)
- the variable input size would also be a bit troublesome to deal with - what would be a good representation for the input to the neural network?
- the training data may also be a bit hard to obtain since it would need ships to be found first so that the training data can be inputted which would mean this can't be used to search for ships with new speeds? It would also need quite a number of ships of that speed to be found so...
I am considering trying to implement this in the latter half of this year when I'm more free but I don't really know how to resolve these issues. Any ideas? Or maybe there is another way to identify which extension is more promising?
BSFKL, Extended Generations, Regenerating Generations, Naive Rules, R1 Moore, R2 Cross and R2 Von Neumann INT
And some others...
E.g., when searching for vertical or horizontal branches, general search direction matters. This I do manually by setting up specific searches for parts. AI could learn to set these parameters and use feedback reports on search progress for measuring success of specific parameter settings.
https://catagolue.hatsya.com/census/b3a ... a4ity6c/C1
Working on a spaceship search program…
Stop turning this forum into a place for politics. Please.
Code: Select all
- Posts: 1819
- Joined: December 15th, 2017, 12:05 am
- Location: Unidentified location "https://en.wikipedia.org/wiki/Texas"
For example, B-like rules have metastable c/2o engines resembling the B-heptomino, as is the case in regular Life. They usually have S4i to stabilize this frontend, and to make the R-pentomino evolve into a B-like object; and no S3a, which tends to drastically stabilize rules because the block vanishes instantly.
In parts of this rulespace, B-like frontends can be much more long-lived than in Life, and some rules have (predecessors to) high-period oscillators and spaceships; many of the spaceships found by the 5s project are B-like objects. In the classic B-like rule, Omosso, the following six objects are respectively three different diehards, a p112 RRO, a c/98o spaceship, and a 50c/100o.
Code: Select all
x = 5, y = 45, rule = B2k3acijr4ijqy6i7c/S2aek3ijnqr4it5n 2b2o$bo2bo$o3bo$2ob2o$b2o16$2b2o$bo2bo$bo2bo$2ob2o$b2o16$ob2o$2o2bo$4b o$bob2o$b2o!