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In video games using procedural world generation, the map seed is a (relatively) short number or text string which is used to procedurally create the game world ("map"). "). This means that while the seed-unique generated map may be many megabytes in size (often generated incrementally and virtually unlimited in potential size), it is possible to reset to the unmodified map, or the unmodified ...
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.
Different models can be generated by changing both deterministic parameters and a random seed. In computing , procedural generation is a method of creating data algorithmically as opposed to manually, typically through a combination of human-generated content and algorithms coupled with computer-generated randomness and processing power.
In many applications, the deterministic process is a computer algorithm called a pseudorandom number generator, which must first be provided with a number called a random seed. Since the same seed will yield the same sequence every time, it is important that the seed be well chosen and kept hidden, especially in security applications, where the ...
A counter-based random number generation (CBRNG, also known as a counter-based pseudo-random number generator, or CBPRNG) is a kind of pseudorandom number generator that uses only an integer counter as its internal state. They are generally used for generating pseudorandom numbers for large parallel computations.
If a full derandomization is desired, a completely deterministic simulation proceeds by replacing the random input to the randomized algorithm with the pseudorandom string produced by the pseudorandom generator. The simulation does this for all possible seeds and averages the output of the various runs of the randomized algorithm in a suitable way.
Intuitively, an extractor takes a weakly random n-bit input and a short, uniformly random seed and produces an m-bit output that looks uniformly random. The aim is to have a low d {\displaystyle d} (i.e. to use as little uniform randomness as possible) and as high an m {\displaystyle m} as possible (i.e. to get out as many close-to-random bits ...