Causal Control Models for Fast Adaptation in Dynamic Environments

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Solution Overview

Problem

Existing techniques for determining control settings in dynamic environments are inefficient, requiring extensive computational resources and failing to adapt quickly to changes in environmental relationships, often leading to sub-optimal control and potential deviations from safe settings.

Innovation Solution

A control system that employs a causal model to repeatedly select and monitor control settings, adjusting internal parameters to maintain optimal performance by continuously updating the model based on environment responses, ensuring rapid adaptation to changing conditions.

Engineering Contradictions & Design Principles

VSEngineering 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 extensive computational resources and fails to adapt quickly to changes in environmental relationships

Engineering Contradiction:

Improveadaptability to environmental changesVSAvoidcomputational efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements dynamic adaptation by continuously updating the causal model as environmental relationships change. The system transitions from static historical pattern recognition to dynamic causal inference that adapts in real-time to changing environmental conditions, resolving the contradiction between adaptability and computational efficiency

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the parameter of the control model from passive historical pattern recognition to active causal inference with dynamically updated parameters. This allows the system to adapt to environmental changes while maintaining computational efficiency through focused causal analysis rather than exhaustive pattern matching

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If active control techniques are used for knowledge generation, then the system can rely on controlled experimentation, but the system may deviate from safe settings and requires more time to achieve optimal control

Engineering Contradiction:

Improveprecision of causal knowledgeVSAvoidsafety of control settings

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent implements feedback mechanisms that continuously monitor environment responses and use this information to update the causal model. This feedback loop enables precise causal knowledge acquisition while maintaining safety through continuous validation against actual environmental responses, preventing deviations from safe settings

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary causal analysis before implementing control actions, using the causal model to predict outcomes and guide safe experimentation. This preliminary action ensures that controlled experimentation proceeds within safe boundaries while still achieving precise causal knowledge

Inventive Principle:

Principle #10Preliminary action

3Productivity

If the system uses fixed internal parameters for control, then the system structure is simpler, but the system cannot quickly respond to changes in relationships among variables

Engineering Contradiction:

Improveresponse speed to environmental changesVSAvoidcomplexity of parameter management

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements dynamic internal parameters that automatically adjust as the causal model updates. This dynamic parameter management enables rapid response to environmental changes while the system handles the complexity through automated causal inference mechanisms, resolving the contradiction between response speed and parameter management complexity

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260010131A1Determining causal models for controlling environments

Publication Date: 2026.01.08 3M INNOVATIVE PROPERTIES CO

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.