pbuk said:
Thanks for the probing Jim, but I'm still not sure what
@paulrk is looking for. Does
this paper help (or help formulate a question more precisely)?
It might also help if you could explain
why you want whatever it is that you think you want: the shortest route from A to C is not always via B.
I’ve already seen that paper but it’s not what I’m searching for, although it is certainly very interesting!
Jarvis323 said:
I am not sure exactly what you mean.
Kolmogorov complexity is a measure of complexity for a single instance of a binary string. If you take the configuration of the game of life at some iteration n as the binary string, then you could consider the K-complexity of that string. The K-complexity is the length of the shortest program (counting any input) that will print that string. K-complexity is in general un-computable. That doesn't mean that you can't compute the k-complexity in some instances, only that you are not guaranteed to be able to compute it, and there is no general algorithm you can apply to it. Also K-complexity is dependent on the language that is used.
For the game of life, I don't know how interesting it is to compute the K-complexity of a single configuration. It might be the complexity of predicting that configuration from a prior one that is more interesting.
For K-complexity, the worst case is the print program with the output itself just embedded/hard-coded into the program. This would be the case for example, for a uncompressed string.
With the game of life, you have some trick you could use, because you can derive one configuration from any of it's previous configurations, you at least know that the minimal program is one that includes the logic implementing the update rule, along with any previous configuration. That previous configuration could also be compressed, although you need to factor in the decompression algorithm into the complexity.
That does sound interesting though. If I understood you correctly, the trick you are relating to is basically what I meant. Because, what I suggested (adding the amount of operations over n evolutions, translates to what you call "the minimal program is one that includes the logic implementing the update rule, along any previous configuration[...]" but as you mention next, by leveraging that trick I forget about the compressed configurations(the ones that lead to the same result with a different previous configurations).
Does that somehow make sense?
"you need to be clear about complexity" - I honestly thought there was only one meaning. very naive indeed

.
But things start to make sense. I guess it's a misunderstanding on my side. I will just try it the other way around and describe the "value" to you and maybe there is a proper name for that (probably not complexity though lOl :D)
My goal is to rate a certain initial cell configuration over n evolutions. And to start off easy I wanted to rate it by the amount of operations per initial cell configuration over n evolutions. And the name complexity seemed plausible since (at least in my light minded head) a game gets more complex if there are more operations taking place. Maybe a better name would be "information" per game?
pls help .-.
I'm 19 yo and in what you would call high school in Germany and definitely no "theoretic" computer science or math guy so I'm not trusted the concept of "complexity" sorry. Going to be studying cs next year so apperantly there are still new things to learn. Although I'm, as I've said, not a fan of theoretical cs but I guess it's an important component, never needed it though and as an autodidact you don't know things you don't need.
Currently I'm working on a project in which I try to train a reinforced neural net to "play" the game of life. In other words I try to maximize certain characteristics of a game "game of life" (such as [portably wrong name apparently]).
I'm working completely on my own but I try to document everything I do so you can find everything including a README which explains everything on the projects GitHub page:
AiPoweredGameOfLife
btw. here(
noise.gif) you can see the result of a game of life with the amount of operations maximized by the reinforcement learning algorithm. As expected there is noise. It does get really interesting though when I start maximizing other "game of life" characteristics such as entropy or the "Algorithmic Specified Complexity in the Game of Life" with the final goal to have a potent ai "game of life" research platform to find stable life.
Note that everything, the characteristics to maximize as well as the game of life are exchangeable, that's what makes this setup so great for "research".
greets from Franconia Germany