Autonomous Agent Update Interval Adjustment for Trust and Cost
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Solution Overview
Problem
Autonomous agents face challenges in maintaining trust with human users due to the tradeoff between frequent status updates, which increase trust but incur high monitoring costs, and infrequent updates, which reduce costs but may lead to reduced trust and suboptimal results.
Innovation Solution
A computer-implemented method for configuring and operating autonomous agents, which involves launching the agent with an initial update interval, measuring the trust level of the human user, and dynamically adjusting the update interval based on the measured trust level to maximize trust while minimizing monitoring costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent status updates are provided by the autonomous agent, then trust level of the human user is improved, but monitoring costs increase
Solution Approach 1:
The patent applies dynamics by making the update interval adjustable rather than fixed. The system dynamically changes the frequency of status updates based on real-time trust level measurements, transitioning from a static monitoring approach to a adaptive one that responds to user-trust conditions.
Solution Approach 2:
The patent implements feedback by measuring the human user's trust level in the autonomous agent and using this measurement to adjust the update interval. This closed-loop system continuously monitors trust and modifies monitoring frequency accordingly, allowing the system to respond to user perceptions and adjust behavior.
2Loss of energy
If infrequent status updates are provided by the autonomous agent, then monitoring costs are reduced, but trust level and task optimization deteriorate
Solution Approach 1:
The system dynamically adjusts the update interval based on measured trust levels, allowing it to operate with lower monitoring costs when trust is high by reducing update frequency, while increasing monitoring when trust decreases. This dynamic adaptation resolves the contradiction by making monitoring intensity conditional rather than constant.
Solution Approach 2:
The patent changes the parameter of update interval frequency based on the trust level parameter. By adjusting the temporal parameter of status updates according to the psychological parameter of user trust, the system optimizes both monitoring costs and trust formation simultaneously.
3Reliability
If frequent status updates are provided, then trust formation is improved, but agent autonomy value is eroded
Solution Approach 1:
The system dynamically adjusts update frequency to balance trust formation with autonomy preservation. When the agent performs well and trust increases, the system reduces update frequency, thereby maintaining autonomy. When trust decreases, the system increases monitoring, creating a dynamic balance between these competing requirements.
Solution Approach 2:
The feedback mechanism allows the agent to sense user trust levels and adjust its reporting behavior accordingly. This enables the agent to maintain autonomy by reducing updates when trusted, while still forming trust through selective communication, resolving the contradiction between trust formation and autonomy maintenance.
Data Source
AI summary
An autonomous agent operating method, system, and computer program product, including launching a first autonomous agent for a task with an initial update interval and adjusting the initial update interval for a second autonomous agent based on a second task for the second autonomous agent being similar to the task over time in relation to a trust level of a human user in a performance of the first autonomous agent.