Adaptive Autonomy Selection for User-Aware Building Control
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Solution Overview
Problem
Existing home security and automation systems lack the ability to adapt their level of autonomy to the preferences and conditions of individual users, leading to suboptimal interaction and decision-making.
Innovation Solution
A method and system for determining a user's likely condition and selecting an appropriate autonomy level based on historical behavior, user input, and system interactions, allowing the system to make decisions on behalf of the user or allowing the user to make decisions, or a combination of both, through a control unit that includes modules for condition determination, autonomy assignment, and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses fixed rule-based decision processes, then the system operation is simple and reliable, but the system cannot adapt to individual user preferences and conditions
Solution Approach 1:
The system dynamically adjusts the autonomy level based on user conditions and preferences. The control unit transitions from static rule-based operations to dynamic decision-making where the autonomy level can change based on user context, such as location, historical behavior, and real-time conditions, allowing the system to adapt without requiring complete system redesign
Solution Approach 2:
The system changes the parameter of autonomy level (from fixed to variable) based on user conditions. By introducing autonomy level as a可调 parameter that can take different values (e.g., high autonomy, low autonomy), the system achieves adaptability to individual users while maintaining a relatively simple underlying architecture
2Productivity
If the system makes all decisions autonomously, then decision-making efficiency is improved, but user control and satisfaction are reduced
Solution Approach 1:
The autonomy level is dynamic rather than fixed. The system can switch between high-autonomy mode (for efficiency) and low-autonomy mode (for user control) based on user conditions and preferences, balancing productivity and user control across different operational contexts
Solution Approach 2:
Different autonomy levels are applied to different users or different decision contexts based on local user conditions. Instead of a uniform decision-making approach, the system tailors the autonomy level to each user's preferences and situation, allowing high efficiency where appropriate while maintaining user control where needed
3Ease of operation
If the system asks users to make all decisions, then user control is maximized, but decision-making time and user burden increase
Solution Approach 1:
The system dynamically adjusts between requiring user input and making autonomous decisions based on the autonomy level. This reduces the time users spend making decisions while preserving user control, as the system only seeks user input when appropriate according to the determined autonomy level and user conditions
4Productivity
If the system uses perceived conditions rather than actual conditions, then the system can operate with available information, but decision accuracy may be reduced
Solution Approach 1:
The system performs preliminary actions by making decisions based on perceived conditions when actual conditions are not immediately available. This allows the system to operate proactively using available information (maintaining productivity) while still being able to refine decisions when actual condition data becomes available
Data Source
AI summary
Methods and systems are described for selecting a level of autonomy. According to at least one embodiment, a method for selecting a level of autonomy includes determining a first likely condition of a first user of a task and/or building system, selecting a first autonomy level from multiple levels based at least in part on the first likely condition, and making a first decision in the task and/or the building system based on the first autonomy level.