A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems
This addresses set addition problems for applications like game deck building, but it is incremental as it compares existing architectures on a specific domain.
The paper tackles the problem of evaluating element additions to sets by modeling preferences based on decision context, comparing two Siamese network architectures on a Magic: The Gathering card preference task, where the triplet network outperforms the twin network and both beat previous results.
In this paper, we study the problem of evaluating the addition of elements to a set. This problem is difficult, because it can, in the general case, not be reduced to unconditional preferences between the choices. Therefore, we model preferences based on the context of the decision. We discuss and compare two different Siamese network architectures for this task: a twin network that compares the two sets resulting after the addition, and a triplet network that models the contribution of each candidate to the existing set. We evaluate the two settings on a real-world task; learning human card preferences for deck building in the collectible card game Magic: The Gathering. We show that the triplet approach achieves a better result than the twin network and that both outperform previous results on this task.