Data exploration, Playing styles, and Gameplay for Cooperative Partially Observable games: Pictionary as a case study

Kiruthika Kannan


Cooperative human-human communication becomes challenging when restrictions such as difference in communication modality and limited time are imposed. In this thesis, we present the popular cooperative social game Pictionary as an online multimodal test bed to explore the dynamics of humanhuman interactions in such settings. Pictionary is a multiplayer game where the players attempt to convey a word or phrase through drawing. The restriction imposed on the mode of communication gives rise to intriguing diversity and creativity in the players’ responses. To explore the player activity in Pictionary, an online browser-based Pictionary application is developed and utilized to collect a Pictionary dataset. We conduct an exploratory analysis of the dataset, examining the data across three domains: global session-related statistics, target word-related statistics, and user-related statistics. We also present our interactive dashboard to visualize the analysis results. We identify attributes of player interactions that characterize cooperative gameplay. Using these attributes, we find stable role-specific playing style components independent of game difficulty. In terms of gameplay and the larger context of cooperative partially observable communication, our results suggest that too much interaction or unbalanced interaction negatively impacts game success. Additionally, the playing style components discovered via our analysis align with select player personality types proposed in existing frameworks for multiplayer games. Furthermore, this thesis explores atypical sketch content within the Pictionary dataset. We present various baseline models for detecting such atypical content. We conduct a comparative analysis of three baseline models, namely BiLSTM+CRF, SketchsegNet+, and modified CRAFT. Results indicate that the image segmentation-based deep neural network outperforms recurrent models that rely on stroke features or stroke coordinates as input.

Year of completion:  November 2023
 Advisor : Ravi Kiran Sarvadevabhatla

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