Recognition of Partial Drawing Sequences for Constructing an AI Player in Drawing Werewolf

Drawing-based social deduction games require artificial intelligence (AI) agents to infer semantic information from incomplete and evolving visual inputs under asymmetric information conditions. In this study, we address the problem of recognizing drawing targets from partial sketch sequences toward constructing an AI player for Drawing Werewolf, a collaborative drawing game derived from the Werewolf (Mafia) genre. Using stroke-based representations from the “Quick, Draw!” dataset, we formulate incremental sketch classification as a sequence modeling task and compare a unidirectional Long Short-Term Memory (UniLSTM) model with a Transformer-based model under realistic online inference constraints. Experiments were conducted on 44 animal classes, evaluating classification accuracy at different drawing stages ranging from one stroke to completed sketches. The results demonstrate that both models improve as additional strokes are observed; however, the Transformer consistently outperforms UniLSTM across all stroke counts. The performance gap is particularly pronounced in early-stage prediction, where sketches are highly incomplete and ambiguous. Class-wise analyses further reveal that the advantage of self-attention depends on visual characteristics and drawing progression. These findings indicate that self-attention mechanisms are well suited for modeling partial sketch sequences and provide valuable insights for designing AI players capable of real-time inference in drawing-based social deduction games.