Bot Confidence Score Calibration for Multi-Bot Selection
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Solution Overview
Problem
Existing communication systems face challenges in accurately categorizing user intent and selecting appropriate bots for diverse user inquiries due to non-standardized confidence scores and unintended impacts from feedback systems, leading to suboptimal user experiences.
Innovation Solution
A calibration system adjusts bot confidence scores using locally relevant feedback, employing a mapping process that corrects scores independently of bot modifications, enhancing bot selection accuracy and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feedback systems are used to modify or update bots, then bot performance can be improved, but unintended impacts on bot operation occur and complexity increases
Solution Approach 1:
The system separates the feedback processing function into a distinct calibration system that operates independently from the bot operation. The calibration system receives feedback data, processes it through a mapping process, and generates calibration scores that are then applied to bot confidence scores. This segmentation allows feedback to be incorporated without directly modifying bot operation, avoiding unintended impacts while maintaining improved bot performance.
Solution Approach 2:
The calibration system acts as an intermediary between feedback systems and bot operation. Instead of feeding feedback directly to bots (which causes unintended impacts), the calibration system processes feedback through a mapping process that adjusts confidence scores without altering bot behavior. This intermediary layer enables performance improvement while isolating bots from the complexity of feedback processing.
2Adaptability or versatility
If multiple bots are used to handle diverse user inquiries, then user experience can be improved, but accurately selecting the appropriate bot becomes more difficult
Solution Approach 1:
The system transforms raw confidence scores from multiple bots into calibrated scores by applying calibration factors derived from feedback data. This parameter transformation allows the system to compare and select among multiple bots more accurately. The calibration process adjusts the confidence scores to reflect actual performance rather than raw predictions, enabling better bot selection across diverse user inquiries while maintaining system adaptability.
3Productivity
If bot confidence scores are used to select bots, then automated selection is efficient, but scores are not standardized and accuracy is reduced
Solution Approach 1:
The calibration system incorporates feedback data to continuously improve the accuracy of bot confidence scores. By processing feedback through a mapping process, the system generates calibration factors that adjust raw confidence scores to better reflect actual bot performance. This feedback mechanism maintains automated selection efficiency while progressively improving score accuracy without requiring manual intervention in bot operation.
Data Source
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AI summary
The present disclosure relates generally to facilitating two-way communications. One example involves receiving input data as part of a two-way communication session associated with a plurality of bots, accessing confidence scores from the bots. Mapped scores are then generated for the plurality of bots from the confidence scores using a bot score mapper. A selected bot is identified using the mapped scores, and the two-way communication session is facilitated using the selected bot. Further, techniques are provided to track performance of the selected bot and dynamically updated mapping adjustments in the bot score mapper using feedback and machine learning systems.