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Extracting Semantic Information from Dynamic Graphs of Geometric Data
n this paper, we demonstrate the utility of dynamic network sequences to provide insight into geometric data; moreover, we construct a nat- ural syntactic and semantic understanding of these network sequences for useful downstream applications. As a proof-of-concept, we study the trajectory data of basketball players and construct “interaction networks” to express an essential game mechanic: the ability for the offensive team to pass the ball to each other. These networks give rise to a library of player configurations that can in turn be modeled by a jump Markov model. This model provides a highly compressed representation of a game, while capturing important latent structures. By lever- aging this structure, we use a Transformer to predict trajectories with increased accuracy.
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- Award ID(s):
- 2006125
- NSF-PAR ID:
- 10320710
- Date Published:
- Journal Name:
- 10th International Conference on Complex Networks and their Applications
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract When developing models in cognitive science, researchers typically start with their own intuitions about human behavior in a given task and then build in mechanisms that explain additional aspects of the data. This refinement step is often hindered by how difficult it is to distinguish the unpredictable randomness of people’s decisions from meaningful deviations between those decisions and the model. One solution for this problem is to compare the model against deep neural networks trained on behavioral data, which can detect almost any pattern given sufficient data. Here, we apply this method to the domain of planning with a heuristic search model for human play in 4-in-a-row, a combinatorial game where participants think multiple steps into the future. Using a data set consisting of 10,874,547 games, we train deep neural networks to predict human moves and find that they accurately do so while capturing meaningful patterns in the data. Thus, deviations between the model and the best network allow us to identify opportunities for model improvement despite starting with a model that has undergone substantial testing in previous work. Based on this analysis, we add three extensions to the model that range from a simple opening bias to specific adjustments regarding endgame planning. Overall, our work demonstrates the advantages of model comparison with a high-performance deep neural network as well as the feasibility of scaling cognitive models to massive data sets for systematically investigating the processes underlying human sequential decision-making.
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Accepted Manuscript1.0
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