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For Minecraft especially, there are websites [1] [2] [non-primary source needed] and articles, [3] [4] dedicated to sharing seeds which have been found to generate interesting maps. The effect of loading a map originally generated in Minecraft 1.6.4 in Minecraft 1.7.2. The map seed is unchanged, but the map generation algorithm has changed ...
The levels in Spelunky are procedurally generated by rearranging premade tiles of geometry into a level with an entrance, exit, a solvable path between the two, and obstacles to that path. [2] Other games procedurally generate other aspects of gameplay, such as the weapons in Borderlands which have randomized stats and configurations.
Rust ' s development began as a clone of DayZ, [28] a popular survival mod for ARMA 2, featuring elements derived from Minecraft. [29] Garry Newman, the CEO of Facepunch Studios, said "Rust started off as a DayZ clone. But then we decided that we are sick of fighting zombies. And can't compete with the Arma island in terms of landmarks and towns."
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Random maps typically have a certain theme - for example a naval random map with many small islands, or a 'gold rush' map with a large amount of gold in the center of the map. The type of random map may also influence the game's artificial intelligence, with the AI employing different strategies optimized for each random map.
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator. A pseudorandom number generator's number sequence is completely determined by the seed: thus, if a pseudorandom number generator is later reinitialized with the same seed, it will produce the same sequence of numbers.
It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...
In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and the uniform distribution.
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