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Efficient forward model for Super Mario AI framework
 Šosvald, David ; Gemrot, Jakub (advisor) ; Dingle, Adam (referee)
The artificial intelligence framework for Super Mario Bros. has been used in a lot of research in the past decade. We have noticed that the forward model (simulation of the game world) present in the framework is quite inefficient and thus negatively influences all work that is based on it, especially intelligent agents that utilize it. This means that every work using such agents is influenced too. That might also include level generation, where agents are often used to test the playability and properties of levels. We have implemented a more efficient forward model and as a proof of concept, we used the improved forward model to create new intelligent agents. In a benchmark we ran, the negative influence of the original forward model was confirmed, as all agents using the new forward model greatly outperformed agents based on the original one.
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