Automated Video Game Environment Validation via Neural Network Agents
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Solution Overview
Problem
Existing approaches to validating video game environments are often time-consuming and require specialized knowledge in domains like programming and machine learning, limiting their efficiency and accessibility.
Innovation Solution
A computer-implemented method that involves providing demonstrations of a video game entity interacting with the environment, generating training examples from these demonstrations, training an agent using a neural network to control the entity, and performing validation by controlling the entity in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing validation approaches are used, then validation can be performed, but the process is time-consuming and requires specialized knowledge
Solution Approach 1:
The system enables automated self-validation of video game environments through neural network agents that autonomously navigate and detect bugs without human intervention. The agent independently performs validation tasks by exploring the environment, identifying issues, and reporting bugs, eliminating the need for specialized human validators and significantly reducing validation time.
2Ease of operation
If existing validation approaches are used, then validation can be performed, but specialized knowledge in programming and machine learning is required
Solution Approach 1:
The neural network agent performs validation autonomously without requiring human expertise in programming or machine learning. The system encapsulates complex AI capabilities within the automated agent, allowing users with minimal technical knowledge to initiate and review validation results through a user-friendly interface.
Solution Approach 2:
The neural network agent acts as an intermediary between the validation system and the user. It translates complex environment validation tasks into actionable bug reports that users can understand and review, bridging the gap between sophisticated AI technology and end-user accessibility.
3Measurement precision
If comprehensive validation is performed, then bug detection accuracy is improved, but training time and data requirements increase
Solution Approach 1:
The system performs preliminary training of the neural network agent using synthetic data and pre-collected environment information before actual validation begins. This preliminary preparation enables the agent to achieve high bug detection accuracy without requiring extensive training time during the validation process itself.
Solution Approach 2:
The system uses a combination of synthetic training data and selective real-world validation data, applying partial exhaustive testing strategies. This approach achieves comprehensive bug detection accuracy by focusing validation efforts on high-risk areas while using synthetic data to cover edge cases that would be time-consuming to encounter naturally.
Data Source
AI summary
This specification provides a computer-implemented method comprising providing, by a user, one or more demonstrations of a video game entity interacting with a video game environment to achieve a goal. The method further comprises generating, from the one or more demonstrations, one or more training examples. The method further comprises training an agent to control the video game entity using a neural network. The training comprises generating one or more predicted actions for each training example by processing, using the neural network, input data derived from the training example, and updating parameters of the neural network based on a comparison between the one or more predicted actions of the training examples and the one or more corresponding target actions of the training examples. The method further comprises performing validation of the video game environment, comprising controlling the video game entity in the video game environment using the trained agent.