Tactical Software Controller for Human-Like Command Generation
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Solution Overview
Problem
Existing systems fail to generate commands that accurately replicate human operator behavior in complex mission scenarios, as existing human behavior models are not adaptive and do not consistently respect scenario constraints, leading to inefficient and costly simulations.
Innovation Solution
A command generation system utilizing tactical and technical software automata that switch states based on mission events, generating commands that adapt to evolving scenarios and environments, respecting mission constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a simulator is used to train operators for complex missions, then training effectiveness is improved, but the complexity of the training system increases
Solution Approach 1:
The training system is segmented into multiple independent modules: a mission event library storing standardized event definitions, a simulator executing scenarios, and a command generator producing training commands. This segmentation allows each component to be developed, tested, and maintained independently, reducing overall system complexity while maintaining training effectiveness.
Solution Approach 2:
The system uses a universal command structure where the same command generator can produce commands for different mission events and training scenarios by simply loading different event definitions from the library. This multi-functionality reduces the need for separate training systems for different scenarios, thereby reducing complexity while improving training effectiveness.
2Measurement precision
If detailed simulation data is processed to generate training commands, then command accuracy is improved, but processing time increases
Solution Approach 1:
Mission event definitions and simulation data processing rules are established in advance before actual training runs. The system pre-defines how simulation data should be interpreted and converted into commands, so that during actual training, the command generator can quickly process data without performing complex analysis in real-time, thus maintaining accuracy while reducing processing time.
Solution Approach 2:
The system creates a simplified representation of mission events in the library that captures essential characteristics without all the complexity of raw simulation data. By working with these simplified event definitions rather than raw data, the command generator can produce accurate commands faster, reducing processing time while maintaining command accuracy.
Data Source
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AI summary
The system (10) comprises at least one tactical software controller (40A to 40E), capable of switching from an initial tactical state to at least one modified tactical state upon identification, from simulation data received from a receiving interface (38), of a mission event contained in a mission event library (44). It comprises at least one technical software controller (42A to 42F), the technical controller (42A to 42F) being capable of activating upon switching the tactical controller (40A to 40E) into the modified tactical state, and being capable of switching from an initial technical state to at least one modified technical state upon identification, from simulation data, of a mission event contained in the mission event library (44). The technical controller (42A to 42F) is capable, upon switching into the modified technical state, of generating at least one command for the function to be controlled.