Game Engine Injecting Human Intelligence Tasks
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Solution Overview
Problem
Existing systems for distributing human intelligence tasks, such as visual identification, are inefficient due to high labor costs and errors, especially when the economic value of the tasks is low.
Innovation Solution
A gaming system that injects human task data into games, allowing players to interact with digital image or audio data and provide human intelligence input, which is then analyzed for consistency and accuracy to infer solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators are paid to perform visual identification tasks, then task completion rate improves, but labor cost increases
Solution Approach 1:
The system allows players to voluntarily perform identification tasks during game play without direct payment. The game environment itself serves as the recruitment and task distribution mechanism, eliminating the need for paid labor while maintaining task completion through player engagement and competition.
Solution Approach 2:
The game system serves multiple functions simultaneously: entertainment, player engagement, and data collection for identification tasks. By integrating task performance into the core game loop, the system eliminates the need for separate paid task execution while maintaining high completion rates through player motivation.
2Measurement precision
If human operators perform identification tasks manually, then accuracy improves for complex patterns, but error rate increases due to carelessness or intentional mistakes
Solution Approach 1:
The system implements consistency checking by comparing results from multiple independent players. When players consistently identify the same pattern or object, the result is validated as accurate. This feedback mechanism eliminates errors from carelessness or intentional mistakes while maintaining high accuracy for complex pattern recognition.
Solution Approach 2:
The system collects identification results from multiple players rather than relying on a single operator. This excessive action approach ensures that even if some players make errors, the consistent majority vote produces accurate results, thereby reducing the overall error rate while maintaining high identification accuracy.
3Extent of automation
If traditional distributed computing systems are used for task processing, then automation improves, but human intelligence input is lost
Solution Approach 1:
The system merges automated task distribution and result collection with human intelligence input during game play. The game engine automatically distributes identification tasks to players and collects results, maintaining high automation while preserving human capability for recognizing complex patterns, emotions, and contextual nuances that algorithms cannot detect.
Data Source
AI summary
A game engine is configured to accept human intelligence tasks as in-game content and present the in-game content to the game player. A method performed by the game engine enables performance of human intelligence tasks, such as visual discrimination, in a video game context. The game engine may receive a definition of human intelligence tasks from one or more remote sources. The game engine may present the human intelligence tasks to multiple video game participants as in-game content. The game engine defines and enables game play rules for the in-game content. The game play rules set parameters for the multiple video game participants to perform the human intelligence tasks to achieve desired results. The game engine may award each of the multiple video game participants an improved game score upon successful performance of the human intelligence tasks in accordance with the game play rules. The game engine may measure success by consistency in responses between different participants or trials.