This project was inspired by a talk given by Jens and Jadga at Robolot 2024. The talk was shared online here https://forum.snap.berkeley.edu/t/robolot-2024/16717/2 and can be found at https://www.youtube.com/watch?v=0ft0J3rh3WY&t=1926s I learned from Jens how to create blocks that predict the next word in a sentence, given the three previous words, based on his 4-grams language model. My goal is to create basic drawings, perhaps reminiscent of Mondrian's style, using just this limited set of instructions. The challenge is to achieve variety with such a small "corpus" (meaning I'm starting with only one example 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 project was inspired by a talk given by Jens and Jadga at Robolot 2024. The talk was shared online here https://forum.snap.berkeley.edu/t/robolot-2024/16717/2 and can be found at https://www.youtube.com/watch?v=0ft0J3rh3WY&t=1926s I learned from Jens how to create blocks that predict the next word in a sentence, given the three previous words, based on his 4-grams language model. My goal is to create basic drawings, perhaps reminiscent of Mondrian's style, using just this limited set of instructions. The challenge is to achieve variety with such a small "corpus" (meaning I'm starting with only one example drawing).

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image0.5Edges: I had to disrupt the model's natural generation process by introducing an external rule that made the turtle bounce back towards the center when it reached the edge of the drawing 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