Neural Network Player Presence Simulation
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Solution Overview
Problem
Computer games often lack a satisfying multiplayer experience due to limitations in single-player modes, where players may not always be able to play against their preferred opponents, leading to a perception of a limited experience.
Innovation Solution
A computer-implemented method using a neural network to generate a player presence in a computer-generated gaming environment, allowing for the creation of a simulated player presence based on the gameplay data of another player, enabling a more immersive single-player experience by training the neural network using supervised, unsupervised, or reinforcement learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single-player mode is used, then the player can play anytime without needing other players, but the gaming experience becomes limited and less engaging
Solution Approach 1:
The patent creates a copy of another player's gaming behavior through a neural network model. The system captures gameplay data from a second player and uses it to train a neural network that replicates their decision-making patterns, playing style, and interaction methods. This allows the first player to experience multiplayer engagement by interacting with a realistic simulation of their preferred opponent, even when that opponent is not actually present.
2Adaptability or versatility
If a multiplayer mode is used, then the gaming experience is more engaging and competitive, but the player cannot always play against their preferred opponents
Solution Approach 1:
The system performs preliminary action by capturing and storing gameplay data from the second player before they are needed. The neural network is trained in advance on this recorded data, creating a pre-built behavioral model that can be immediately deployed when the first player wants to compete. This eliminates the need for real-time availability of the original player while maintaining the authenticity of their gaming style.
Solution Approach 2:
The patent creates a copy of another player's gaming behavior through a neural network model. The system captures gameplay data from a second player and uses it to train a neural network that replicates their decision-making patterns, playing style, and interaction methods. This allows the first player to experience multiplayer engagement by interacting with a realistic simulation of their preferred opponent, even when that opponent is not actually present.
3Ease of operation
If a traditional AI opponent is used, then the game can be played in single-player mode, but the opponent lacks realistic player-like behavior and adaptability
Solution Approach 1:
The patent replaces traditional rule-based AI mechanics with a neural network-based system. Instead of using predetermined algorithms and decision trees, the system uses a machine learning model that has learned player behavior patterns from actual gameplay data. This substitution enables the opponent to exhibit realistic, adaptive behavior that mirrors human players while maintaining full automation for single-player mode.
Data Source
AI summary
A method and system is provided which trains a neural network which can be used to provide a player presence in a computer generated gaming environment.