Meta-Environment Change Detection via Parameter Weighting
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Solution Overview
Problem
Forecasting system behavior in meta-environments is challenging due to changes in rulesets and abilities, making it difficult to predict outcomes in e-sports, sports, and robotic interactions, as historical data becomes unreliable when conditions change.
Innovation Solution
A computer-implemented method and system that collect historic and current parameter values from actors in a meta-environment, determine weight values, and detect changes by comparing these values using a function exceeding a threshold, allowing for 'back-casting' of data to account for rule changes and generating new parameter values to enrich historical data for accurate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical data is used for forecasting system behavior, then prediction can be made based on past performance, but the prediction becomes unreliable when meta-environment changes occur
Solution Approach 1:
The patent implements dynamic detection of meta-environment changes by continuously monitoring parameter values and comparing them against threshold criteria. When changes are detected, the system dynamically adjusts its forecasting approach, transitioning from relying on historical data to using real-time data, thereby maintaining prediction reliability across changing conditions
Solution Approach 2:
The system employs feedback mechanisms by detecting changes in the meta-environment and using this information to adjust the forecasting process. The change detection unit monitors parameter values and provides feedback that triggers appropriate responses, such as adjusting weight values or switching data sources, to maintain reliable predictions despite environmental changes
2Adaptability or versatility
If the ruleset or abilities of actors change in the meta-environment, then new conditions can be adapted, but historical data becomes less reliable for forecasting
Solution Approach 1:
The patent applies preliminary action by detecting meta-environment changes before they significantly impact forecasting accuracy. The system proactively identifies changes in rulesets or actor abilities and adjusts its data collection and analysis approaches in advance, preventing the degradation of historical data reliability
Solution Approach 2:
The system changes parameters by adjusting the weight values assigned to different data sources based on detected meta-environment changes. When changes are detected, the system modifies the importance given to historical versus current data, thereby adapting to new conditions while maintaining forecasting reliability
3Measurement precision
If more parameter values are collected to improve forecasting accuracy, then prediction precision increases, but the complexity of data processing increases
Solution Approach 1:
The patent extracts only the most relevant parameter values needed for accurate forecasting by using change detection to identify which parameters have actually changed in the meta-environment. This selective extraction approach maintains forecasting accuracy while reducing the overall complexity of data processing by focusing on critical changes rather than processing all possible parameters
Data Source
AI summary
A computer-implemented method for detecting a change in a meta-environment is disclosed. The method comprises collecting historic parameter values from at least one actor being active in the meta-environment, wherein each of the at least one actors has a set of abilities represented by parameters relating to the collected parameter values, collecting current parameter values from the at least one actor being active in the meta-environment, determining weight values of the collected historic parameters and weight values of the collected current parameters, and upon a value of a function of the determined weight values of the historic parameters and the weight values of the collected current parameters being greater than a predefined threshold value, determining a detection of the change in the meta-environment.