期刊
ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS
卷 11, 期 3-4, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3387165
关键词
Algorithm visualization; game visualization; dimensionality reduction; trajectories; multivariate time series
资金
- State of Upper Austria
- Austrian Federal Ministry of Education, Science and Research via the LIT-Linz Institute of Technology [LIT-2019-7-SEE-117]
- Austrian Research Promotion Agency [FFG851460]
- Austrian Science Fund [FWF P27975-NBL]
- State of Upper Austria (HumanInterpretable Machine Learning)
In problem-solving, paths to solutions can be seen as sequences of decisions that can be visualized through dimensionality reduction. By analyzing patterns in these trajectories, general statements about problem-solving tasks and strategies can be made, which is crucial for understanding human and machine decisions in various application domains.
In problem-solving, a path towards a 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 bemade 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.
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