ML In-Game Help Sessions for Real-Time Gameplay Assistance
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Solution Overview
Problem
Video game players often face difficulties in overcoming challenging game situations and rely on rudimentary methods like online forums or videos for help, which can be time-consuming and disruptive to their gaming experience.
Innovation Solution
Implementing a computer-implemented method using a trained machine learning model that analyzes prior gameplay data from help sessions to provide automated assistance during current gaming sessions, utilizing various machine learning frameworks such as support vector machines, decision trees, and neural networks to generate inputs and outputs for the game.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If players use online forums or videos for help, then they can get assistance from the gaming community, but the process is time-consuming and disruptive to their gaming experience
Solution Approach 1:
The system enables self-service by having the machine learning model automatically analyze gameplay data, identify difficult situations, and generate helpful inputs without requiring players to manually search forums or videos. The model serves itself by processing game state information and autonomously generating assistance inputs that can be applied during gameplay.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on extensive gameplay data and help session patterns before actual gameplay occurs. This pre-training allows the model to quickly and accurately identify difficult situations and generate appropriate help inputs during real-time gameplay without requiring players to spend time learning or searching for help resources.
2Reliability
If players seek help from other players, then they can overcome difficult gaming situations, but it interrupts their gaming flow and experience
Solution Approach 1:
The machine learning model acts as an intermediary between the player and the gaming community knowledge. Instead of players directly interacting with forums or videos, the model mediates by automatically processing gameplay data, identifying difficult situations, and generating synthesized help inputs that can be applied during gameplay, thus maintaining gaming continuity.
Solution Approach 2:
The system replaces the mechanical system of manual help-seeking (browsing forums, watching videos) with an automated machine learning-based system that continuously processes gameplay data and generates help inputs in real-time, eliminating interruptions to gaming flow while maintaining problem-solving capability.
3Measurement precision
If a machine learning model is trained on all prior gameplay data, then the model can provide accurate assistance, but the training process is computationally intensive and time-consuming
Solution Approach 1:
The system applies segmentation by dividing the training process into manageable segments: first training on aggregated gameplay data to learn general patterns, then fine-tuning on specific help session data. This segmented approach allows the model to achieve high accuracy for specific difficult situations without requiring computational resources to process every single piece of gameplay data simultaneously.
Solution Approach 2:
The system extracts only the most relevant information from prior gameplay data by identifying and isolating help sessions that occurred during difficult gaming situations. This extraction process filters out unnecessary data, allowing the model to be trained on a curated subset of high-value examples that maximize help accuracy while minimizing computational cost.
4Productivity
If the system provides automated help during gameplay, then players receive quick assistance, but the system complexity increases
Solution Approach 1:
The machine learning model is designed with multi-functionality, serving multiple purposes: analyzing gameplay data, identifying difficult situations, generating help inputs, and adapting to different game states. This universal approach consolidates multiple functions into a single system component, reducing overall system complexity while maintaining high help delivery speed.
Data Source
AI summary
The disclosed concepts relate to training a machine learning model to provide help sessions during a video game. For instance, prior video game data from help sessions provided by human users can be filtered to obtain training data. Then, a machine learning model can be trained using approaches such as imitation learning, reinforcement learning, and/or tuning of a generative model to perform help sessions. Then, the trained machine learning model can be employed at inference time to provide help sessions to video game players.