Gaming Bot Simulation for User Experience Modeling
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Solution Overview
Problem
Game development faces challenges in efficiently testing and optimizing gaming applications due to the vast number of possible deck combinations and player interactions, requiring extensive manual testing and analysis, which is time-consuming and labor-intensive.
Innovation Solution
The game development platform employs gaming bots and BEA tools to automate testing, predict player motivations, and generate new content, utilizing machine learning algorithms and procedural content generation to simulate player interactions and analyze game telemetry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing and analysis is used to test all possible deck combinations and player interactions, then testing completeness is improved, but time consumption and labor requirements increase significantly
Solution Approach 1:
The patent creates virtual copies of players (gaming bots) that simulate human player behavior and interactions. These bots replicate player actions, deck strategies, and gameplay patterns to comprehensively test the game without requiring actual human players for every test scenario, thereby reducing time and labor while maintaining testing completeness
Solution Approach 2:
The system performs preliminary automated testing of deck combinations and player interactions before actual game deployment or before manual testing begins. By pre-testing various scenarios with gaming bots and analyzing potential issues in advance, the system identifies problems early without requiring extensive manual testing time later
2Manufacturing precision
If manual testing of all deck combinations is performed, then game balance accuracy is improved, but productivity decreases due to extensive manual effort required
Solution Approach 1:
The system implements self-service automated testing where gaming bots independently execute test scenarios, collect data, and provide feedback without requiring continuous human intervention. The automated analysis tools process test results and identify balance issues autonomously, maintaining high accuracy while dramatically improving testing efficiency and productivity
Solution Approach 2:
The patent incorporates feedback mechanisms where gaming bots play test games, their performance data is automatically collected and analyzed, and results are used to identify deck balance issues. This automated feedback loop continuously refines game balance accuracy without requiring manual analysis of each test scenario, thereby maintaining precision while enhancing productivity
3Loss of information
If extensive manual analysis of game telemetry data is conducted, then player experience understanding is improved, but labor requirements and time investment increase
Solution Approach 1:
The patent replaces manual analytical processes with automated machine learning algorithms and data analysis tools. These computational systems process game telemetry data, player behavior patterns, and experience metrics automatically, substituting human analytical effort with automated computational analysis that maintains deep player experience understanding while reducing labor requirements and simplifying the analysis process
Data Source
AI summary
In various embodiments, a method is presented that includes: generating, via a system including a processor, behavioral experience analysis (BEA) tools based on a preference learning model; receiving, via the system, game telemetry data from a gaming application; generating, via the system, a predicted user motivation by applying the BEA tools to the game telemetry data; and facilitating adaptation of the gaming application based on the predicted user motivation.