Social Agent Model Update via Active Feedback Timing
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Solution Overview
Problem
Current electronic social agents lack the ability to adaptively customize their responses to changes in user and environment states, leading to cumbersome user experiences due to inadequate consideration of user and environment data in customization processes.
Innovation Solution
A method and system for updating a decision-making model of an electronic social agent by actively collecting user responses in near real-time, using sensors to determine the desirability of additional feedback, generating questions at optimal times, and updating the model based on collected responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If currently-available social agents provide adaptive customization features, then the agent can learn user preferences and provide personalized interactions, but the agent fails to detect and respond to changes in user state and environment state, resulting in outdated customization
Solution Approach 1:
The system implements continuous feedback loops where the social agent collects user responses and environment data, processes this information through reinforcement learning, and updates its decision-making model accordingly. This allows the agent to detect changes in user state and environment state and adapt its customization dynamically, resolving the contradiction between providing adaptive customization and reliably responding to changes.
Solution Approach 2:
The social agent performs self-customization by automatically updating its own decision-making model based on collected user feedback and environment data. This self-service mechanism enables the agent to maintain up-to-date customization without requiring manual intervention, thereby reliably adapting to changes while providing adaptive customization features.
2Manufacturing precision
If the social agent actively collects user feedback through questions, then the customization quality improves, but the user experience becomes cumbersome when questions are presented during busy periods
Solution Approach 1:
The system dynamically adjusts the timing and frequency of feedback collection based on real-time assessment of user state and environment conditions. The reinforcement learning model determines optimal moments to present questions, avoiding busy periods while maximizing customization quality. This dynamic approach resolves the contradiction by making feedback collection adaptive rather than static or forced.
Solution Approach 2:
The system performs preliminary assessment of user state and environment data before initiating feedback collection. By evaluating conditions in advance, the system determines whether it is an appropriate time to present questions, thereby maintaining customization quality while avoiding disruption during busy periods.
3Measurement precision
If the social agent collects extensive user data and environment data, then the customization becomes more accurate, but the system complexity increases
Solution Approach 1:
The system changes parameters of the decision-making model based on collected data, using reinforcement learning to adjust model parameters dynamically. This approach maintains high customization accuracy by continuously optimizing model parameters based on user feedback and environment data, while avoiding the need for excessively complex data processing architectures.
Solution Approach 2:
The reinforcement learning component acts as an intermediary between raw data collection and customization output. It processes user responses and environment data through learned policies, transforming complex data inputs into actionable customization decisions. This intermediary layer manages system complexity while maintaining measurement precision for accurate customization.
Data Source
AI summary
According to some disclosed embodiments an action is performed by an electronic social agent. The electronic social agent collects a first dataset indicating the user's state, the user's environment state, and a first user response to the performed action. Then, it is determined whether it is desirable to collect a second response from the user and, if so, it is further determined whether to generate a question to be presented to the user based on an analysis of a first dataset and the first user response. Then, an optimal time for presenting the question to the user is determined. A question that is based on the collected data and the first user response is generated by the electronic social agent for actively collecting an additional user response. Then, based on the collected additional user response, the decision-making model of the electronic social agent is updated and improved.