Causal Environment Control With Adaptive Parameter Updating
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Solution Overview
Problem
Existing techniques for controlling environments are limited in determining optimal control settings due to reliance on modeling-based methods that require extensive data and computational resources, and active control methods that may not adapt quickly to changing conditions.
Innovation Solution
A system that selects control settings based on a causal model identifying relationships between control settings and environment responses, continuously adjusting internal parameters to optimize performance and adapt to changes, using recursive experimental control and multi-objective optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modeling-based techniques are used to control the environment, then the system can learn control patterns from historical data, but the system requires extensive data and computational resources
Solution Approach 1:
The patent segments the control problem into multiple procedural instances grouped by shared characteristics. Each instance can be controlled independently with its own causal model, dividing the overall computational burden into manageable segments while maintaining control accuracy for each segment
Solution Approach 2:
The system dynamically adapts by learning causal relationships from data and updating control decisions in real-time. The causal models are continuously refined as new data becomes available, allowing the system to adjust to changing conditions without requiring complete retraining on extensive historical datasets
2Measurement precision
If active control techniques are used for knowledge generation, then the system can perform controlled experimentation, but the system may not adapt quickly to changing conditions
Solution Approach 1:
The system performs preliminary controlled experimentation to establish causal models before actual control operations. These pre-learned causal relationships enable rapid adaptation to changing conditions without requiring continuous experimentation, as the system can quickly query the existing causal models for optimal control decisions
Solution Approach 2:
The system implements continuous feedback loops where control outcomes are monitored and used to update causal models. This feedback mechanism allows the system to adapt to changing conditions by refining its understanding of causal relationships while maintaining the precision gained from controlled experimentation
3Adaptability or versatility
If the system continuously monitors and adjusts control settings, then the system can respond to environment changes, but the system may increase computational resource usage
Solution Approach 1:
The patent merges multiple control decisions into procedural instances that share common characteristics and can be controlled together. By combining related control actions and leveraging shared causal models, the system reduces redundant computational operations while maintaining comprehensive monitoring and adjustment capabilities
Solution Approach 2:
The system applies partial monitoring and adjustment by focusing computational resources on the most critical control parameters and procedural instances. Rather than uniformly processing all control settings, the system identifies and prioritizes key factors that have the greatest impact on control outcomes, reducing overall computational energy while maintaining effective response to changes
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting control settings for the environment based on (i) a causal model that identifies causal relationships between possible settings for controllable elements in the environment and environment responses that reflect a performance of the control system in controlling the environment and (ii) current values of a set of internal parameters; and during the repeatedly selecting: monitoring environment responses to the selected control settings; determining, based on the environment responses, an indication that one or more properties of the environment have changed; and in response, modifying the current values of one or more of the internal parameters.