Meningkatkan Kesulitan Serangan Enemy Dengan Menambahkan Influence Map Pada Metode A* Pada Procedural Generated Tower Defense Game

Michael Budiono(1*), Liliana Liliana(2), Hans Juwiantho(3),


(1) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(2) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(3) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(*) Corresponding Author

Abstract


In tower defense game, if the map dan enemy attack do not change, the strategy that will be used by the player will remain the same, this will make the game having a low replay value and will make the player stop playing the game. The existing tower defense game have used procedural generation to create different levels each time they are played, but there are still shortcoming where the map looks plain and has the same pattern every time it is played, other than that enemy attack using A* have a simple pattern and can’t  search for a profitable path for the enemy so the enemy become easy to defeat and the game become less interesting. To overcome this problem, perlin noise is used in procedural generation so that the map mode does not look plain and does not have the same pattern every time it is played, other than that enemy attack use A* by adding influence map so that enemy attack can be more challenging and interesting to the player.

In this thesis, the map was created using perlin noise to determine the terrain of the tile on the map and the location of start and finish will be checked using floyd warshall algorithm to determine if the map need to be remade. For enemy attack, A* is used with the addition of influence map to make the enemy can choose a path that is more profitable for it by avoiding roads blocked by tower and roads that can be attacked by towers.

The test results show that after the map is generated repeatedly for 20 times, no map has the same location and distance between start and finish. In addition, it is also found that the implementation of procedural generation and influence map made the game 25.7% more challenging when compared to games that did not use it.


Keywords


Procedural Content Generation; Perlin Noise; Influence Map; Pathfinding; A*

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References


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