AI Game Customization for Dynamic Paytable Updates
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Solution Overview
Problem
Electronic games become boring for veteran players, leading to a loss of interest and reduced playtime, as existing solutions like paytable patches and new game development are time-consuming and costly with limited effectiveness.
Innovation Solution
Customizing electronic games using AI models trained on player activity to generate and apply game characteristics, such as paytables, based on player behavior and preferences, allowing for dynamic updates to maintain player engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If paytable patches and updates are deployed to keep games fun, then player interest is maintained, but development time and cost increase
Solution Approach 1:
The system dynamically generates paytable characteristics in real-time based on player activity and model predictions, rather than using static paytables. This allows the game to adapt and refresh player interest without requiring manual development updates, resolving the contradiction between maintaining player interest and reducing development time
Solution Approach 2:
The game system automatically generates and updates its own paytable characteristics using the trained model and player activity data, without requiring external developer intervention. This self-service capability eliminates the time and cost burden of manual paytable creation while continuously maintaining player engagement
2Reliability
If new games are developed to replace older ones, then player interest is renewed, but development cost and time increase
Solution Approach 1:
Instead of creating entirely new games, the system changes key parameters of existing games (paytable characteristics) based on model predictions and player behavior. This approach renews player interest through parameter variation rather than full game development, significantly reducing complexity and cost
3Device complexity
If paytable characteristics are fixed, then game structure is simple, but player interest decreases over time
Solution Approach 1:
The system transitions from static paytable characteristics to dynamic, adaptive characteristics that change based on player activity and model predictions. This maintains relatively simple game structure while continuously adapting parameters to sustain player engagement, resolving the contradiction between simplicity and engagement
Data Source
AI summary
Embodiments of the present disclosure are directed to customizing an electronic game. Generally speaking, one or more trained models of electronic game characteristics can be applied by a host system using Artificial Intelligence (AI) to generate customizations for one or more characteristics of an electronic game. The generated characteristics can be further based on and/or in response to tracking game play information of a particular player for which they are generated. For example, if the player has slowed or otherwise changed play of the game indicating a possible loss of interest in the game, new characteristics can be generated and applied to the game to renew interest by the player. In other cases, the new characteristics can be generated and applied in response to a request from the player.