POI Classification Using Player Feedback in Parallel Reality Games
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Solution Overview
Problem
Existing parallel reality games lack sufficient rich data for classifying real-world points of interest, which hinders the integration of meaningful virtual elements and activities, limiting player engagement and real-world interaction.
Innovation Solution
A machine learning model is employed to classify points of interest using training data sets, incorporating verified properties and player inputs to predict and verify properties for points of interest, enabling the server to classify them for specific activities within the game.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a parallel reality game uses basic location data without rich properties, then the game can be implemented with simple data requirements, but the integration of meaningful virtual elements and player engagement is limited
Solution Approach 1:
The system performs preliminary classification of points of interest using a machine learning model before game elements are placed. Training data sets are generated in advance containing verified properties, and the model is trained beforehand to predict properties for new locations, enabling meaningful virtual element integration without requiring complete rich data for every location upfront.
Solution Approach 2:
The system implements feedback loops where player inputs and verification data are continuously fed back into the machine learning model. Players can verify or correct predicted properties, and this feedback is used to retrain and improve the model's accuracy over time, progressively enriching the data available for point of interest classification.
2Measurement precision
If the system manually verifies properties for all points of interest, then classification accuracy is high, but the time and resources required are excessive
Solution Approach 1:
The system applies partial verification by using the machine learning model to predict properties for all points of interest, then selectively verifying only those predictions that fall below a confidence threshold. This approach achieves sufficient accuracy for game purposes without the excessive time cost of manual verification for every single property.
Solution Approach 2:
The machine learning model serves itself by automatically predicting properties for new points of interest without requiring manual intervention. The system autonomously classifies locations based on trained patterns, only engaging human verification when the model's confidence is insufficient, thereby reducing overall verification time while maintaining acceptable accuracy.
3Reliability
If the machine learning model uses only verified properties from training data, then prediction accuracy is reliable, but the model cannot adapt to new or unverified player inputs
Solution Approach 1:
The system dynamically adjusts its approach by initially relying on verified training data for reliable predictions, then progressively incorporating unverified player inputs as the model adapts. The model evolves from a static, verification-dependent system to a dynamic one that can handle and learn from unverified data, improving both reliability and adaptability over time.
Solution Approach 2:
Player inputs and verification results are fed back into the training process, allowing the model to adapt to new patterns and properties. This feedback mechanism enables the system to maintain reliability by learning from verified corrections while becoming more versatile in handling diverse and unverified player-submitted properties.
Data Source
AI summary
A machine learning model classifies points of interest in a parallel reality game hosted by a server. The server generates training data sets that include verified properties for points of interest. The machine learning model may predict unverified properties for points of interest. Players in the parallel reality game may input properties for the points of interest. The machine learning model use the received properties from players as inputs to the machine learning model to verify unverified properties or generate new properties for the points of interest. The server may classify the points of interest as suitable for particular activities, and the server may use the classifications for future activities within the parallel reality game.