ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making Paths
This work addresses the need for better visualization tools to understand complex decision-making processes in various domains, though it appears incremental by extending existing trajectory analysis methods.
The paper tackled the problem of analyzing decision-making paths in problem-solving by visualizing multiple trajectories in a shared embedding space, rather than focusing on single trajectories, and demonstrated this approach across domains like Rubik's cube, chess, and neural network training to make general statements about tasks and strategies.
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.