Causal Environment Control With Adaptive Temporal Attribution
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Solution Overview
Problem
Existing techniques for determining control settings in dynamic environments are limited by their reliance on modeling-based approaches that require extensive historical data and are slow to adapt to changes, or active control methods that are resource-intensive and lack precision.
Innovation Solution
A control system that automatically generates causal knowledge through real-time monitoring and adjustment of internal parameters, allowing for rapid adaptation to environmental changes while optimizing control settings based on a causal model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If modeling-based techniques are used to determine control settings, then the system can learn from historical data, but the system is slow to adapt to changes and requires extensive historical data
Solution Approach 1:
The patent implements dynamic procedural instances with adjustable temporal extents that adapt to changing environmental conditions. The system dynamically adjusts the temporal extent parameters based on observed changes in environmental responses, allowing the causal model to capture both short-term and long-term effects as needed. This dynamic structure enables rapid adaptation to environmental changes while maintaining the ability to learn from historical patterns.
Solution Approach 2:
The patent segments the environment into multiple procedural instances, each with its own temporal extent parameters. This segmentation allows the system to independently track and adapt to changes in different procedural contexts simultaneously, improving overall adaptability without requiring the entire system to react uniformly to changes.
2Measurement precision
If active control techniques are used for knowledge generation, then the system can actively control the environment, but the system is resource-intensive
Solution Approach 1:
The patent applies partial action by selectively adjusting temporal extent parameters only for procedural instances where changes are detected, rather than continuously monitoring and adjusting all parameters. The system identifies specific procedural instances that require attention and focuses computational resources on those areas, reducing overall resource consumption while maintaining precision where needed.
Solution Approach 2:
The system uses feedback from environmental responses to dynamically adjust temporal extent parameters. By monitoring changes in environment responses and using this feedback to refine the causal model selectively, the system achieves high measurement precision without requiring continuous full-system active control, thereby reducing computational resource requirements.
3Speed
If the system continuously monitors and adjusts control settings, then the system can respond rapidly to changes, but the system requires extensive computational resources and data
Solution Approach 1:
The patent establishes causal models and temporal extent parameters in advance for multiple procedural instances. When environmental changes occur, the system can rapidly respond by adjusting pre-configured parameters rather than building models from scratch, enabling fast response while reducing the need to process extensive new data for each change.
Solution Approach 2:
The system achieves rapid response by changing temporal extent parameters rather than fundamentally reprocessing data. By adjusting the temporal extent parameters of existing procedural instances based on detected changes, the system can quickly adapt to new conditions without requiring extensive new data collection and processing.
4Adaptability or versatility
If the system uses a fixed temporal extent for procedural instances, then the system is simpler to implement, but the system cannot adapt to changes in the duration of environmental effects
Solution Approach 1:
The patent implements dynamic temporal extent parameters that can be adjusted based on observed environmental responses. The system starts with baseline temporal extents and progressively adjusts them as it learns the actual duration of effects for different procedural instances, enabling adaptability to temporal changes while managing complexity through incremental learning.
Solution Approach 2:
The system adds the dimension of temporal adaptability by introducing adjustable temporal extent parameters without fundamentally changing the core causal modeling structure. This allows the system to adapt to varying effect durations while maintaining the simplicity of the underlying causal model framework.
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; determining a temporal extent for the procedural instance based on temporal extent parameters for the one or more entities in the procedural instance; selecting control settings for the procedural instance; monitoring environment responses to the control settings that are received for the one or more entities; determining which of the environment responses to attribute to the procedural instance in a causal model; and adjusting, based at least in part on the environment responses that are attributed to the procedural instance, the temporal extent parameters for the one or more entities.