Introduction to Learning in Games |
Posted: September 14, 2017 |
To create a fun and successful game you need to be able to challenge your players. They need to feel that they are overcoming something by beating your game. One way to achieve this is to have your game learn from their actions, a slot games may contain conform to the game itself. Have it analyze what they are doing and try to come up with counter attacks to provide a challenge and to create the illusion of more intelligent opponents. Pattern Matching There are a lot of legitimate artificial intelligence algorithms to finding patterns in data and finding patterns in reactions to them based on different success requirements. However, for the purposes of game design a lot of these are currently over kill, and more importantly, not tuned to the scope of the problem. You are not interested in creating a perfect reactionary machine in a game enemy, you are interested in provide a challenge for the player. Any game already has a big plus for you as the designer, since it is your creation and you know the limits of the game. You can therefore build your own pre-made patterns and test for them by checking the player's input or different aspects of how they are playing. For instance, in a fighting game such as Street Fighter 2, the player has six buttons they can choose from. By capturing when the player hits these buttons and the distance of the enemy or if the enemy is in the air, you can find certain patterns of play. The player may often try to punch and then move in for a throw when they are close enough. The player may always try to do an uppercut when their enemy has jumped. By recording different input and game information at the time of input you can create a map of possible actions that you can use for the game's AI. In doing so you can "learn" the players moves and then try to counter them. Real-time strategy (RTS) games have a much more complex system of attack, because the input of mouse clicks are irrelevant. To try to learn what your player is attempting to do in an RTS game you will have to abstract the data of the player's actions to find a common pattern. This is a totally game dependent process, but as an example let's use Command & Conquer (C&C). In C&C the objective of an average mission is to build troops and a base to defend yourself, then destroy the enemy and their base. There are two necessary points of learning: how the player interacts with enemy units and how the player builds his base. To keep this example in focus we will only explore the first learning objective although the second would be crucial for counter-attacks. Contact between C&C's units is very limited, when they are close enough together then they will begin to fight each other. The first type data you will need to search on is the player's preferred unit types. The player may prefer doing tank rushes; in this case, you will need to build defenses that specialize in defeating tanks. If they prefer making mini-gunner units then you will adjust your defenses against that type of attack. The player could have a preference of attacking the harvesters versus attacking the base directly. This can be recorded and used so that you can send out troops to guard the harvesters or build more protection around the main base. To create a good learning system you need to find the most common methods of attack and then figure out how you can determine if they are occurring.
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