The authors divide this problem into 2 subproblems: 1/ Building formations with high synergy 2/ Solve puzzles in a near-optimal way
@maximelabonne
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Optimizing Puzzle Generation: Beyond Exhaustive Search Methods
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Current landscape: • Traditional approaches to generating puzzles are often time-consuming • Exhaustive search may not be feasible due to the large design space
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Graph Genetic Algorithm Optimizes Video Game Puzzle Validation
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Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design A funny evolutionary algorithm for solving puzzles in video games from @EA It doesn't only save time for designers but also helps to improve puzzle quality #AI https://
arxiv.org/abs/2302.09040 -
Social Distancing Rules in 24 Dimensions
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Hahaha imagine social distancing rules in 24 dimensions. So close yet so far…
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24-Dimensional Hypersphere Packing Problem in Computing
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When you stack hyperspheres in 24 dimensions, a sphere can touch 196,560 other hyperspheres and nobody likes it.
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Learnable Prompts Emerge as Key GNN Pre-training Architecture
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In conclusion, it looks like a neat architecture to pre-train GNNs. Learnable prompts are a handy addition and might re-appear in future architectures. Can't wait to become a graph prompt engineer.
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Learnable Prompt Vectors Reduce Parameters for Downstream Tasks
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It only uses the learnable prompt vector and not the GNN weights, which are frozen for downstream tasks. This reduces the number of parameters that need to be updated, improves the computational efficiency of task learning/inference, and reduces the reliance on labeled data.
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Prompt Tuning Optimizes Downstream Task Efficiency and Accuracy
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Prompt tuning involves optimizing the learnable prompt to improve the computational efficiency and accuracy of downstream tasks. It is based on the similarity of subgraphs and is formulated using prompt-assisted task-specific subgraph representations.
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Learnable Prompts Enable Better Task-Specific Knowledge Extraction
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Learnable prompts provide a better alternative to handcrafted prompts, and enable the extraction of the most relevant prior knowledge for each task. The prompts are a dimension-wise reweighting (or a linear transformation) of the node representations.
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Graph Neural Networks Pre-training with Link Prediction Task
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During pre-training, the GNN is trained on a link prediction task. By sampling nodes and forming triplets, a pre-training loss is constructed that improves the similarity between the contextual subgraphs of two candidate nodes.