AI Anomaly Detection Using Expected Ranges in Chaotic Environments
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Solution Overview
Problem
Existing artificial intelligence systems lack the capability to effectively detect and respond to anomalies in chaotic environments, where sensor data exhibits pseudo-Brownian motion, which can be indicative of non-Brownian motion caused by external interference.
Innovation Solution
An artificial-intelligence system comprising a central server and autonomous agents that receive sensor readings, determine expected ranges of variables, and trigger mitigation actions when anomalies are detected outside these ranges, distinguishing between pseudo-Brownian and non-Brownian motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AI systems are used to monitor chaotic environments, then general anomaly detection is possible, but they cannot effectively distinguish between pseudo-Brownian motion (random fluctuations) and non-Brownian motion (actual anomalies)
Solution Approach 1:
The system changes the parameter of analysis by computing expected ranges based on historical sensor data and statistical models of Brownian motion. By comparing current sensor readings against these dynamically computed expected ranges, the system can distinguish between normal random fluctuations (pseudo-Brownian motion) and actual anomalies (non-Brownian motion), thereby improving detection accuracy while maintaining reliability.
2Speed
If the system monitors all sensor data in real-time to detect anomalies, then response time is improved, but computational complexity and resource usage increase
Solution Approach 1:
The system applies partial action by computing expected ranges only for variables that show significant deviations from their historical patterns. Rather than performing full statistical analysis on all sensor data continuously, the system monitors all data but triggers detailed analysis only when necessary, thus improving response time while controlling computational complexity.
3Measurement precision
If the expected range for variables is set narrowly to detect subtle anomalies, then detection sensitivity is improved, but the system becomes more prone to false alarms from normal fluctuations
Solution Approach 1:
The system uses feedback by continuously monitoring sensor readings and comparing them against expected ranges that are derived from historical data. When a variable falls outside its expected range, the system triggers an anomaly detection event. The expected ranges are dynamically adjusted based on the statistical properties of the chaotic system, allowing the system to maintain high sensitivity while adapting to normal fluctuations, thereby reducing false alarms.
Data Source
AI summary
A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory. The memory stores instructions that cause the processor to receive a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording pseudo-Brownian change in one or more variables in the chaotic environment; determine, based on the first set of sensor readings, an expected range of the one or more variables during a second time window after the first time window; receive a second set of sensor readings from the one or more remote electronic sensors during the second time window recording change in the one or more variables; determine, based on the second set of sensor readings, that one variable of the one or more variables is not within the expected range; and cause the one or more autonomous agent devices to attempt to mitigate a potential harm indicated by the one variable being outside of the expected range.