A Holistic Framework for Multimodal Ecosystem of Pictionary

Nikhil Bansal


In AI, the ability of intelligent agent to model human player in games such as Backgammon, Chess and Go has been an important metric in benchmarking progress. Fundamentally, the games mentioned above can be characterized as competitive and zero-sum. In contrast, games such as Pictionary and Dumb Charades falls into the category of ‘social’ games. Unlike competitive games, the emphasis is on cooperative and co-adaptive game-play in a relaxed setting. Such social games can form the basis for the next wave of game-driven progress in AI. Pictionary™ is a wonderful example of cooperative game play to achieve a shared goal in communication-restricted settings. This popular sketch-based guessing game, which we employ as a use case, provides an opportunity to analyze shared goal cooperative game play in restricted communication settings. To enable the study of Pictionary and to understand various aspects associated with the game play, we designed a software ecosystem for web-based online game of Pictionary dubbed PICTGUESS. To overcome several technological and logistic barriers, which the actual game presents, we implemented a simplified setting for PICTGUESS wherein a game consists of a time-limited episode involving two players - a Drawer and a Guesser. The Drawer is tasked with conveying a given target phrase to a counterpart Guesser by sketching on a whiteboard within that time limit. However, occasionally some players in Pictionary draw atypical sketch content. While such content is occasionally relevant in the game context, it sometimes represents a rule violation and impairs the game experience. To address such situations in a timely and scalable manner, we introduce DRAWMON, a novel distributed framework for automatic detection of atypical sketch content in concurrently occurring Pictionary game sessions. We build specialized online interfaces to annotate atypical sketch content, resulting in ATYPICT, the first ever atypical sketch content dataset. We use ATYPICT to train CANVASNET, a deep neural atypical content detection network. We utilize CANVASNET as a core component of DRAWMON. Our analysis of post deployment game session data indicates DRAWMON’s effectiveness for scalable monitoring and atypical sketch content detection. Beyond Pictionary, our contributions can also serve as a design guide for customized atypical content response systems involving shared and interactive whiteboards.

Year of completion:  September 2023
 Advisor : Ravi Kiran Sarvadevabhatla

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