Application Testing Service for Policy Evaluation
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Solution Overview
Problem
Traditional application testing is slow and expensive, making it inefficient for identifying effective policies that provide a customized user experience based on contextual information.
Innovation Solution
A method and system for training computer-implemented decision policies that allows users to evaluate the effectiveness of hypothetical policies by displaying actual performance results and computing reward statistics using experimental data, enabling comparison and identification of more effective policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional application testing methods are used to evaluate policies, then policy effectiveness can be determined through actual implementation, but the testing process becomes slow and expensive
Solution Approach 1:
The system performs preliminary actions by collecting and storing experimental data from actual policy implementations before evaluating new hypothetical policies. This pre-collected data serves as a foundation for rapidly evaluating multiple policies without requiring full-scale A/B tests for each one, thus accelerating the testing process while maintaining evaluation accuracy.
Solution Approach 2:
The system creates copies of policy evaluation results by using experimental data to simulate and predict the performance of hypothetical policies. Instead of implementing each policy in real-time and measuring actual user interactions, the system computes reward statistics based on historical experimental data, effectively copying the evaluation process and significantly reducing testing time and cost.
2Reliability
If traditional application testing methods are used to evaluate policies, then reliable performance results can be obtained, but testing costs increase
Solution Approach 1:
The system uses copying by computing reward statistics for hypothetical policies based on existing experimental data rather than conducting new expensive A/B tests. This allows reliable performance predictions to be generated at a fraction of the cost of traditional testing methods.
Solution Approach 2:
The system performs preliminary data collection and storage during normal operation, building a repository of experimental data that can be reused for evaluating multiple policies. This preliminary action eliminates the need to repeatedly conduct expensive experiments for each policy evaluation, reducing overall testing costs while maintaining result reliability.
3Adaptability or versatility
If multiple policies are tested through traditional methods, then effective policies can be identified, but the process becomes time-consuming
Solution Approach 1:
The system efficiently evaluates multiple policies by copying the evaluation framework and applying it to different hypothetical policies using the same experimental data. This allows rapid comparison of many policies without the time penalty of traditional sequential testing, enabling the evaluation of numerous policies in parallel.
Solution Approach 2:
The system achieves universality by creating a multi-functional evaluation platform that can assess any hypothetical policy against the same experimental data using a standardized reward function approach. This universal framework eliminates the need for policy-specific testing setups, dramatically reducing the time required to evaluate multiple diverse policies.
Data Source
AI summary
The claimed subject matter includes techniques for providing an application testing service with a user interface that enables a user to evaluate performance data for computer implemented decision policies. An example method includes displaying a first reward statistic comprising an actual performance result for a policy implemented by an application. The method also includes obtaining experimental data corresponding to previously implemented policies, computing a second reward statistic for a hypothetical policy using a reward function applied to the experimental data. The method also includes displaying the second reward statistic together with the first reward statistic to enable a user to compare the first reward statistic and the second first reward statistic.