Game level refinement difficulty adjustment method

Abstract

The invention discloses a game level refinement difficulty adjustment method. The method includes the following steps that 1, designed game levels are provided; 2, the average level of actual playersis simulated through artificial intelligence, and thus simulating operation of the game levels is facilitated; 3, according to the result of the simulating operation, a factor library is created; 4, according to the expected difficulty of the game levels, the difficulty of the game levels is dynamically adjusted through the factor library. The game level refinement difficulty adjustment method caneliminate the disadvantages of a current game with the game levels in game level adjustment, and more precise, high-precision and automatic adjustment can be achieved.

Description

technical field [0001] The invention relates to the field of game design, in particular to a method for fine-tuning difficulty adjustment of level-based games. Background technique [0002] At present, there is a problem in making casual games, that is, when the levels designed by the level designers are actually played by players, the results may not be the same as the expected results. For example, imagine that the clearance rate of this level is 25%, but the results obtained according to the actual data analysis are often quite different from the expectations, and such results are often uncontrollable, and this uncontrollable result will also affect the Player experience will also have a greater impact. [0003] Moreover, if the game is adjusted after the game is launched, no matter whether it is increasing the difficulty or reducing the difficulty, the accuracy of the error is not higher than 1%. [0004] If you want the game to achieve the same result as expected, usi...

AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned defects in the prior art, the technical problem to be solved by the present invention is to provide a refined difficulty adjustment method based on level-type games, which can address the disadvantages of level adjustment existing in level-type games (such as adjusting the coarse-grained difficulty level). , need to spend a lot of manpower for evaluation and other issues to optimize), so that finer, high-precision, and automated adjustments can be made

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