AI Exposure Response Using Weighted Risk and Autonomous Agents
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Solution Overview
Problem
Current systems lack effective methods to anticipate and mitigate risks from chaotic environments, where unpredictable changes can lead to significant harm or catastrophic losses in various engineering, computing, and social systems.
Innovation Solution
An artificial-intelligence system comprising a central server and autonomous agent devices that receive sensor data from a chaotic environment, calculate weighted risk exposure, and manipulate resources to minimize exposure harm by reallocating or adjusting resources in response to exceeding risk thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If systems are designed with high risk tolerance to survive extreme events, then system reliability is improved, but resource allocation efficiency deteriorates due to over-preparation for unlikely scenarios
Solution Approach 1:
The patent implements dynamic resource allocation by continuously adjusting resource distribution based on real-time risk assessment. The system transitions from static over-preparation to dynamic adaptation, allocating resources proportionally to actual risk levels rather than maximum possible scenarios, thus improving efficiency while maintaining reliability.
Solution Approach 2:
The system changes the parameter of resource allocation from fixed to variable based on risk exposure calculations. By modifying allocation parameters dynamically according to environmental conditions and risk assessments, the system optimizes the balance between reliability and resource efficiency.
2Loss of information
If distributed sensor systems are deployed to monitor chaotic environments, then environmental awareness is improved, but system complexity increases due to data processing requirements
Solution Approach 1:
The patent extracts only the critical risk-relevant information from the vast sensor data stream, filtering out unnecessary details. By taking out only the essential risk indicators needed for decision-making, the system maintains high environmental awareness while reducing processing complexity.
Solution Approach 2:
The system introduces an intermediary risk assessment layer that processes sensor data and translates it into actionable risk metrics. This intermediary processing stage simplifies the complexity by providing a structured framework for interpreting raw sensor information.
3Reliability
If autonomous agents manipulate resources to minimize risk exposure, then risk mitigation is improved, but response time deteriorates due to calculation and decision-making delays
Solution Approach 1:
The system performs preliminary risk assessments and pre-calculates potential resource allocation scenarios in advance. By preparing response options beforehand, the system can execute risk mitigation actions more quickly when actual threats are detected, reducing response time while maintaining effective risk mitigation.
Solution Approach 2:
The patent implements continuous feedback loops that monitor risk exposure and automatically trigger resource reallocation when thresholds are exceeded. This feedback mechanism enables rapid automated responses without requiring lengthy decision-making cycles, improving response time while maintaining reliable risk mitigation.
Data Source
AI summary
An artificial-intelligence system for manipulating resources to minimize exposure harm in a chaotic environment, comprising autonomous agent devices, remote electronic sensors, and a central server. The central server receives a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording values of one or more variables in the chaotic environment; receives a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum permitted risk exposure for the time interval; determines a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment; determines that the weighted total risk exposure exceeds the maximum permitted risk exposure; and causes the autonomous agent devices to manipulate the one or more resources to decrease the weighted total risk exposure.