ProjectionPathExplorer：探索投影决策路径中的视觉模式
ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making Paths
摘要：
在解决问题的过程中，通往解决方案的路径可以被视为一系列决策。由人类或计算机做出的决策描述了通过问题的高维表示空间的轨迹。通过降维，这些轨迹可以在低维空间中可视化。这种嵌入式轨迹以前曾被应用于各种各样的数据，但分析几乎完全集中在单一轨迹的自相似性。相比之下，我们描述的模式从绘制许多轨迹-不同的初始条件，最终状态和解决方案的策略-在同一嵌入空间。我们认为，一般的陈述解决问题的任务和解决策略，可以通过解释这些模式。我们探索和特征化的轨迹模式，从人类和机器在各种应用领域的决策：逻辑难题（魔方），战略游戏（国际象棋），优化问题（神经网络训练）。我们还讨论了适当选择的表示空间和相似性度量的嵌入的重要性。
英文摘要：
In problem-solving, a path towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction, these trajectories can be visualized in lower-dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but analysis has focused almost exclusively on the self-similarity of single trajectories. In contrast, we describe patterns emerging from drawing many trajectories -- for different initial conditions, end states, and solution strategies -- in the same embedding space. We argue that general statements about the problem-solving tasks and solving strategies can be made by interpreting these patterns. We explore and characterize such patterns in trajectories resulting from human and machine-made decisions in a variety of application domains: logic puzzles (Rubik's cube), strategy games (chess), and optimization problems (neural network training). We also discuss the importance of suitably chosen representation spaces and similarity metrics for the embedding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
