CognitionNet: A Collaborative Neural Network for Play Style Discovery in Online Skill Gaming Platform
🤖 AI Summary
This work addresses the challenge of modeling player behavior in online skill-gaming platforms (e.g., Rummy) by proposing a two-stage collaborative neural network framework that automatically discovers fine-grained behavioral patterns and stable, long-term playing styles from large-scale telemetry sequences. Methodologically, it introduces a novel “bridging loss” training mechanism that jointly optimizes latent-space clustering, sequential modeling, and supervised style classification—enabling end-to-end learning of psychological traits, tactical preferences, and engagement attribution. Key contributions include: (1) the first generalizable and interpretable taxonomy of playing styles grounded in behavioral psychology; (2) significant performance gains over state-of-the-art baselines in engagement prediction; and (3) psychologically informed behavioral diagnostics that support player experience understanding, progression analysis, and risk intervention.
Application Category
📝 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. The complex sequences of intricate sequences is analysed through a novel collaborative 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 (e.g., transitions across 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.
Problem
Research questions and friction points this paper is trying to address.
Discovering player psychology and tactics from gaming telemetry data
Automating play style identification via neural network analysis
Enhancing player engagement predictions with diagnostic explanations
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-stage deep neural network for play style discovery
Bridge loss enables collaborative network training
Automated player psychology and tactics discovery
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