CognitionNet: A Collaborative Neural Network for Play Style Discovery in Online Skill Gaming Platform

• URL: http://arxiv.org/abs/2505.00325v1
• Date: Thu, 01 May 2025 05:51:19 GMT
• Title: CognitionNet: A Collaborative Neural Network for Play Style Discovery in Online Skill Gaming Platform
• Authors: Rukma Talwadker, Surajit Chakrabarty, Aditya Pareek, Tridib Mukherjee, Deepak Saini,
• Abstract summary: We propose a two stage deep neural network, CognitionNet.<n>The first stage focuses on mining game behaviours as cluster representations in a latent space.<n>The second aggregates over these micro patterns to discover play styles.
• Score: 6.665636945186558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Games are one of the safest source of realizing self-esteem and relaxation at the same time. An online gaming platform typically has massive data coming in, e.g., in-game actions, player moves, clickstreams, transactions etc. It is rather interesting, as something as simple as data on gaming moves can help create a psychological imprint of the user at that moment, based on her impulsive reactions and response to a situation in the game. Mining this knowledge can: (a) immediately help better explain observed and predicted player behavior; and (b) consequently propel deeper understanding towards players' experience, growth and protection. To this effect, we focus on discovery of the "game behaviours" as micro-patterns formed by continuous sequence of games and the persistent "play styles" of the players' as a sequence of such sequences on an online skill gaming platform for Rummy. We propose a two stage deep neural network, CognitionNet. The first stage focuses on mining game behaviours as cluster representations in a latent space while the second aggregates over these micro patterns to discover play styles via a supervised classification objective around player engagement. The dual objective allows CognitionNet to reveal several player psychology inspired decision making and tactics. To our knowledge, this is the first and one-of-its-kind research to fully automate the discovery of: (i) player psychology and game tactics from telemetry data; and (ii) relevant diagnostic explanations to players' engagement predictions. The collaborative training of the two networks with differential input dimensions is enabled using a novel formulation of "bridge loss". The network plays pivotal role in obtaining homogeneous and consistent play style definitions and significantly outperforms the SOTA baselines wherever applicable.

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