Clustered Causal Control Models for Dynamic Environment Adaptation
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Solution Overview
Problem
Existing techniques for determining control settings in dynamic environments are limited by the need for a priori knowledge, inefficiency in resource usage, and slow adaptation to changing conditions, leading to sub-optimal control and potential deviations from safe settings.
Innovation Solution
A control system that automatically generates causal knowledge through recursive experimentation, adjusting internal parameters to optimize control settings in real-time, using clustering and probabilistic models to adapt to environmental changes and ensure efficient, safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If modeling-based techniques are used to determine control settings, then the system can passively observe historical data and learn patterns, but the system requires a priori knowledge and adapts slowly to changing conditions
Solution Approach 1:
The patent implements dynamic causal models that are continuously updated as new data becomes available, allowing the system to adapt to changing environmental conditions in real-time rather than relying on static historical patterns. The causal model structure evolves dynamically to reflect current system behavior
Solution Approach 2:
The system incorporates feedback loops where environment responses to selected control settings are fed back into the causal model for continuous updating. This closed-loop feedback mechanism enables rapid adaptation by constantly refining causal relationships based on observed outcomes
2Productivity
If active control techniques are used for knowledge generation, then the system can actively control the environment for experimentation, but the system uses more resources and requires more data
Solution Approach 1:
The patent applies partial experimentation by selectively intervening only on specific procedural instances where causal relationships need clarification, rather than universally experimenting across all instances. This partial action approach reduces data requirements while maintaining learning efficiency
Solution Approach 2:
The system segments the environment into procedural instances and applies different control strategies to different segments. By clustering instances and applying targeted active control only where needed, the system reduces overall data requirements while maintaining productive learning
3Adaptability or versatility
If the system continuously updates causal models for all procedural instances, then the system can adapt to environmental changes, but the computational complexity and resource usage increase
Solution Approach 1:
The patent segments procedural instances into clusters based on shared characteristics, and maintains separate causal models only for each cluster rather than for every individual instance. This segmentation dramatically reduces computational complexity while preserving real-time adaptation capabilities
Solution Approach 2:
The system creates universal causal models at the cluster level that serve multiple procedural instances simultaneously. Each cluster-level causal model functions universally for all instances within that cluster, reducing overall computational burden while maintaining adaptability
4Productivity
If the system explores new control settings to optimize performance, then the system can improve system performance, but the system may deviate from safe ranges and produce sub-optimal control periods
Solution Approach 1:
The patent incorporates safety constraints as feedback boundaries that guide the exploration process. When exploring new control settings, the system uses feedback from safety monitors to prevent deviations beyond acceptable ranges, ensuring reliable operation while still enabling performance optimization
Solution Approach 2:
The system carefully manages parameter changes during exploration by making small, controlled adjustments to control settings rather than large leaps. This gradual parameter change approach allows performance optimization while maintaining safety margins and reducing the risk of sub-optimal control periods
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 identifying a procedural instance; selecting control settings for the procedural instance, comprising, for a particular one of the controllable elements: assigning the procedural instance to a cluster for the particular controllable element in accordance with current values of a set of clustering parameters for the particular controllable element; and selecting a setting for the particular controllable element for the procedural instances based on a causal model that is specific to the cluster; obtaining environment responses to the selected control settings that define a value of the performance metric for the procedural instance; and updating, for the particular controllable element, the causal model for the cluster for the controllable element to which the procedural instance was assigned based on the value of the performance metric.