Training System for Decision Accuracy via Skilled Strategy Modeling
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Solution Overview
Problem
Users, especially those with mediocre skills, face challenges in making decisions in stochastic systems with multiple unknowns and partially observable states, leading to mistakes in operations like gaming and emergency response, where understanding user behavior and decision-making is crucial for improving performance.
Innovation Solution
A training system comprising a processor, storage modules, pre-processing modules, and a comparison module that processes logs of skilled users' decisions to generate a multi-dimensional image array, training a model to predict the best decisions for users by comparing their actions with skilled strategy models, and providing personalized upskilling and skill scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users rely on their own decision-making skills in stochastic systems, then they maintain autonomy in decision making, but decision accuracy deteriorates due to lack of skill and inability to handle multiple unknowns
Solution Approach 1:
The patent introduces an intermediary system comprising multiple modules (behavior analysis module, state determination module, decision generation module) that mediates between the user's partial observations and the stochastic system's unknowns. This intermediary processes user behavior data, determines system states, and generates informed decisions, thereby improving decision accuracy without requiring the user to directly handle the complexity of stochastic systems with multiple unknowns.
2Reliability
If the system provides comprehensive decision support to improve user performance, then decision-making quality improves, but the complexity of the training system increases
Solution Approach 1:
The training system is segmented into distinct functional modules: a behavior analysis module that processes user actions, a state determination module that identifies system states, and a decision generation module that produces recommended decisions. This segmentation allows each module to specialize in specific tasks, improving overall system reliability while managing complexity through modular design that enables independent development, testing, and maintenance of each component.
Solution Approach 2:
The system incorporates self-service mechanisms where the behavior analysis module automatically processes user behavior data, the state determination module autonomously identifies system states based on processed data, and the decision generation module independently generates recommendations. This self-service approach reduces the need for manual intervention and complex coordination, thereby improving reliability while keeping the system manageable in terms of complexity.
3Measurement precision
If the system captures and processes detailed user behavior data to generate accurate predictions, then prediction accuracy improves, but data processing requirements and system resources increase
Solution Approach 1:
The patent extracts only the essential and relevant features from detailed user behavior data that are necessary for accurate predictions. The behavior analysis module selectively processes specific behavior patterns and characteristics rather than analyzing all possible data points. This extraction approach maintains high prediction accuracy by focusing on critical features while significantly reducing computational energy consumption by eliminating processing of redundant or less important data.
Data Source
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AI summary
A training system is disclosed here for training a user in a predefined operation to make a best decision in a predefined operation. A first storage module stores a first log of the decisions made by the skilled users during the predefined operation. A first pre-processing module is in communication with the first storage module to pre-process the first log to generate a first multi-dimensional image array of the predefined operation. A training module trains a model based on the first multi-dimensional image array to generate a skilled strategy model. A second storage module stores a second log of decisions made by the user in the predefined operation and pre-processes the second log to generate a second multi-dimensional image array. A comparison module compares the second multi-dimensional image array with the skilled strategy model to generate a prediction of the best decision to be made by the user.