IoT Device Optimization Function for Autonomous Preference Learning
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Solution Overview
Problem
Network connected devices, such as IoT devices, lack full autonomy and require manual operation or rule creation, which becomes cumbersome as user preferences change and conflicts arise, necessitating a dynamic and efficient automation solution.
Innovation Solution
A system that uses machine learning to automatically learn user preferences and behaviors by integrating inputs from various devices and sensors, determining optimal device control settings through an optimization function, and resolving conflicts between automation rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual operation or rule creation is used to control network devices, then user control capability is maintained, but device autonomy and operational efficiency deteriorate
Solution Approach 1:
The system enables devices to automatically control themselves by evaluating sensor inputs against an optimization function to determine optimal outputs, eliminating the need for manual operation or pre-defined rules. The device autonomously adapts to changing conditions and user preferences through continuous optimization.
Solution Approach 2:
The system incorporates continuous feedback loops where sensor data from the environment and device states are constantly evaluated by the optimization function. This feedback mechanism allows the system to learn from outcomes and automatically adjust control decisions, improving autonomy while maintaining adaptability to user needs.
2Extent of automation
If automation rules are manually created to mimic autonomous operations, then some level of automation is achieved, but system complexity and maintenance difficulty increase as user preferences change
Solution Approach 1:
The system replaces static, manually-created automation rules with a dynamic optimization function that continuously adapts to changing user preferences and environmental conditions. The optimization function dynamically adjusts control outputs based on current sensor inputs, eliminating the need to manually update rules as preferences change.
Solution Approach 2:
The system changes the fundamental parameter from discrete automation rules to a continuous optimization function with adjustable parameters. This function can adapt its behavior by modifying its internal parameters based on learned user preferences, providing automation without the complexity of managing multiple individual rules.
3Adaptability or versatility
If multiple automation rules are created to handle different scenarios, then coverage of user needs is improved, but rule conflicts and resolution difficulty worsen
Solution Approach 1:
The system merges multiple discrete automation rules into a single unified optimization function. This function evaluates all sensor inputs and device states simultaneously to determine the optimal control output, eliminating rule conflicts by providing a single decision-making framework that naturally handles multiple scenarios without contradiction.
Data Source
AI summary
A network connected controllable device is automatically controlled. A plurality of inputs that indicate states of a network connected device environment associated with a property of the network connected controllable device to be automatically controlled is received. A plurality of output candidates is evaluated according to an optimization function. The optimization function depends on the plurality of inputs and one or more parameters and the output candidates are associated with candidate settings of the property of the network connected controllable device to be automatically controlled. An output that optimizes the optimization function is selected among the plurality of output candidates.