If your game uses several random numbers for each frame and needs to run at 60 frames per second, a complex random source could be too slow. More complex algorithms can produce better unpredictability at a cost of processing time. Furthermore, when generated numbers are viewed in binary, some of the bits in a number may be more or less predictable depending on the random source’s algorithm. Eventually, the sequence will repeat-exactly how many random numbers a source generates before repeating depends on the algorithm that source employs. But how is randomness quantified? Computerized pseudorandom number generators are based on finite sequences of numbers that appear to have no order or structure. A random number generator should produce unpredictable behavior (or at least the appearance of it). To build robust pseudorandom behavior in a game, you typically want some or all of the following traits: However, not all random sources are created equal, and using a random source poorly can undermine its purpose. Unexpected surprises can make a game more fun for the player behavior that changes with every playthrough can add replay value to a game elements that mimic natural chaotic systems can make a game world more immersive.īuilding a game that relies on elements of chance typically involves the use of a pseudorandom number generator, or random source. There are board games where a die roll determines the player’s movement, card games with shuffled decks, arcade games where enemy creatures appear at unpredictable times, role-playing games where every action has a chance to succeed or fail, open-world games where background characters wander naturally, and many, many more examples. Games are full of mechanics based on chance and probability.
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