Clustered Causal Models for Adaptive Environment Control

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Solution Overview

Problem

Existing techniques for controlling environments lack precision and efficiency in determining causal relationships between control settings and environment responses, particularly in dynamic systems, often requiring extensive computational resources and historical data, and are slow to adapt to changes.

Innovation Solution

A control system that identifies procedural instances within the environment, assigns them to clusters based on clustering parameters, selects control settings using a causal model that identifies causal relationships between settings and performance metrics, and continuously updates these models to optimize control decisions, allowing for real-time adaptation and efficient resource use.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If modeling-based techniques are used to control the environment, then the system can learn from historical data, but the system requires extensive computational resources and historical data, and is slow to adapt to changes

Engineering Contradiction:

Improveprecision in determining causal relationshipsVSAvoidcomputational resources required

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the environment into procedural instances and groups them into clusters based on similarity. Each cluster has its own causal model, allowing the system to process smaller, more manageable subsets of data rather than analyzing all historical data comprehensively. This segmentation reduces computational complexity while maintaining causal inference precision.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary clustering of procedural instances before conducting causal analysis. By pre-grouping similar instances into clusters and establishing cluster-specific models in advance, the system prepares structured knowledge that accelerates real-time decision-making and reduces the computational burden during active control operations.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If modeling-based techniques are used to control the environment, then the system can discover patterns in data, but the system is slow to adapt to changes in dynamic systems

Engineering Contradiction:

Improveprecision in determining causal relationshipsVSAvoidspeed of adaptation to changes

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system dynamically updates causal models for each cluster as new data becomes available, rather than relying on static historical models. The cluster assignments and causal relationships are re-evaluated continuously, enabling the system to adapt quickly to changing environmental conditions while maintaining precise causal inference through the structured cluster framework.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements continuous feedback loops where environment responses to selected control settings are obtained and used to update the causal models for each cluster. This feedback mechanism allows the system to learn from actual outcomes and rapidly adapt its causal understanding to changing conditions, improving both precision and adaptability.

Inventive Principle:

Principle #23Feedback

3Productivity

If active control techniques are used to generate knowledge, then the system can control the environment actively, but the system requires extensive experimentation and time to establish causal relationships

Engineering Contradiction:

Improvespeed of control decision-makingVSAvoidtime required for experimentation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary clustering and establishes causal models for each cluster before active control operations begin. This pre-processing creates a knowledge base that guides subsequent control decisions, eliminating the need for extensive real-time experimentation while maintaining high productivity in control decision-making.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes the parameter organization by grouping procedural instances into clusters based on similarity metrics. This parameter transformation allows the system to generalize from fewer experiments across cluster representatives, reducing the total experimentation time required while maintaining accurate causal inference for individual instances through cluster-specific models.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12055903B2Determining causal models for controlling environments

Publication Date: 2024.08.06 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 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.