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<An Introduction to General Game Learning ⁄ >

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General game learning (GGL) is the idea of developing an algorithm that can learn how to play arbitrary games by interacting with them. The computer is only provided access to a video feed of the game and is allowed to issue commands such as moving the mouse or pressing buttons on a controller. This is very different from traditional game AIs which already know all the rules of the game and are tightly optimized to make intelligent decisions in the specific game they are written to play. Attempting to generalize these systems is the goal of GGL, which is a rich topic and the task of developing an effective game learner will require combining a diverse number of challenging computer science problems. To make the task more tractable, we will sometimes use a semi-supervised learning approach where the AI also takes as input segments of humans playing the game. The AI can use these to determine what kind of actions are likely to affect the game state, what the goal of the game is, or what strategies might be effective in achieving the goal.

We start by sketching the pipeline stages of a game learning system. A good general game learner starts by breaking down each frame of the game into meaningful components such as "the black king" or "a goomba": we call this a symbolic representation of the game. It then needs to learn rules that govern these entities such as "black kings can move to adjacent squares so long as these squares are either empty or contain white pieces and doing so does not cause the black king to be threatened by a white piece" or "goombas move either left or right on horizontal surfaces at a rate of 3 pixels per frame, unless they are not supported by a horizontal surface in which case they move downward at a rate of 7 pixels per frame". Using these rules it can determine how the symbolic representation changes with respect to time or player actions. A learner also needs to guess the goal of the game, such as "capture the black king" or "reach Princess Peach at the end of world 9". Using this knowledge, it can develop a strategy for reaching the goal such as "capture black pieces without losing white pieces" or "move to the right without dying". Finally, a general game learner needs to combine all of this knowledge together to execute intelligent actions that achieve the goal, and continuously update its internal beliefs as it is presented with new observations and evidence.

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