IoT Command Conflict Resolution via User Hierarchy
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Solution Overview
Problem
Existing IoT systems face challenges in managing and resolving conflicting commands, particularly in hierarchical contexts, which can lead to unintended consequences and user dissatisfaction.
Innovation Solution
The proposed solution involves using trained machine learning models to identify conflicts between commands and determine responses based on a physical world hierarchy between users. This includes receiving commands from multiple users, analyzing them for conflicts, and prioritizing commands based on user hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system processes all commands from multiple users equally, then all user inputs are acknowledged, but command conflicts cannot be resolved and system consistency deteriorates
Solution Approach 1:
The system segments command processing by introducing hierarchical levels based on user relationships. Commands are divided into different processing paths depending on whether they conflict with existing commands, and further segmented by the hierarchical rank of users involved. This allows the system to handle multiple user inputs adaptively while maintaining consistency through structured conflict resolution.
Solution Approach 2:
The system performs preliminary analysis to detect potential command conflicts before executing commands. By using machine learning models to predict conflicts in advance and establishing user hierarchies beforehand, the system can proactively resolve conflicts rather than reactively managing them, ensuring system consistency is maintained from the outset.
2Speed
If the system always executes the latest command, then responsiveness is improved, but commands from lower-ranking users may override higher-ranking users causing dissatisfaction
Solution Approach 1:
The system performs preliminary detection of command conflicts using machine learning models before execution. By identifying potential conflicts in advance and determining user hierarchies beforehand, the system can quickly resolve conflicts using pre-established rules, maintaining fast execution speed while ensuring appropriate commands are prioritized based on user rank.
Solution Approach 2:
The system introduces an intermediary conflict resolution mechanism that mediates between competing commands. This intermediary layer analyzes user hierarchies and command conflicts, then determines execution priority, allowing the system to maintain responsiveness while respecting user hierarchies and preventing lower-ranking users from inappropriately overriding higher-ranking users.
3Measurement precision
If the system implements complex conflict resolution logic, then command prioritization accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components that specialize in conflict detection and resolution. These models handle the complex analysis of command conflicts and user hierarchies, allowing the main system to remain relatively simple while achieving high conflict detection accuracy through the specialized intermediary models.
Solution Approach 2:
The machine learning models automatically learn and adapt to conflict patterns and user hierarchies without requiring manual configuration of complex resolution logic. The system trains models on historical data, allowing them to self-serve the complex task of conflict detection and prioritization, reducing the burden on system architects while maintaining high accuracy.
Data Source
AI summary
Embodiments include a method for controlling a device, a voice-controlled device, and a computer program product. One embodiment of the method may comprise receiving a first command from a first user at a device and receiving a second command from a second user at the device, and determining, by a first trained machine learning model, that the second command conflicts with the first command. In response to determining the second command conflicts with the first command, some embodiments may determine, by a second trained machine learning model, a physical world hierarchy between the first user and the second user, determine a response to the second command using the physical world hierarchy. The method may further comprise performing the response. Some embodiments may further comprise identifying, by the second trained machine learning model, a higher ranking user chosen from the group consisting of the first user and the second user.