Autonomous Agent Behavior Calibration with Teaching Fixtures
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Solution Overview
Problem
The development of autonomous agents is hindered by the complexity of tuning and calibrating their behaviors, requiring specialized skills and significant time and resources, which increases costs and extends development timelines.
Innovation Solution
A system and method for calibrating autonomous agents using a teachable behavior model, a controlled environment, and a calibration module to adjust parameters, allowing for the reduction of the difference between observed and target behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional manual tuning methods are used to calibrate autonomous agent behaviors, then development precision can be achieved through skilled programming, but the process requires significant time, specialized skills, and resources, increasing costs and extending development timelines
Solution Approach 1:
The system enables autonomous self-calibration where the agent independently adjusts its own behavior parameters through interaction with the environment and receipt of feedback signals, eliminating the need for manual tuning by skilled programmers while maintaining calibration precision
Solution Approach 2:
The calibration system implements continuous feedback loops where the agent's actions are monitored, performance is evaluated against desired behaviors, and automatic adjustments are made to parameters based on the feedback, enabling precise calibration without manual intervention and significantly reducing development time
2Adaptability or versatility
If complex decision-making algorithms are implemented to achieve sophisticated autonomous behaviors, then agent capability and adaptability are improved, but the complexity of tuning and calibrating these behaviors increases significantly
Solution Approach 1:
The system introduces an intermediary calibration layer that sits between the complex decision-making algorithms and the environment, providing automated parameter adjustment mechanisms that simplify the calibration process while maintaining the sophistication of the underlying algorithms and the agent's adaptability
Solution Approach 2:
The system manages complexity by focusing calibration efforts on adjusting specific behavioral parameters rather than redesigning entire algorithms, allowing sophisticated agent capabilities to be tuned through systematic parameter modification while keeping the overall system structure manageable
3Manufacturing precision
If multiple specialized personnel are involved in the behavioral tuning process to achieve comprehensive calibration, then calibration quality can be maintained across different skill areas, but friction increases and development efficiency decreases
Solution Approach 1:
The automated calibration system performs multiple calibration functions that would traditionally require different specialists (programmers, psychologists, narrative designers) within a single integrated platform, maintaining comprehensive calibration quality while eliminating interpersonal friction and improving development efficiency
Solution Approach 2:
The system consolidates multiple expert functions into self-service capabilities where the calibration system automatically handles parameter adjustments that would otherwise require coordinated input from multiple specialists, maintaining comprehensive calibration quality while significantly improving productivity
Data Source
AI summary
System, method, devices and non-transitory computer-readable medium for calibrating an autonomous agent. A teachable behavior model is calibrated by deploying the autonomous agent in a controlled environment with teaching fixtures. Teachable parameters are altered to reduce the difference between an observed behavior and a target behavior. The autonomous agent is deployed into an uncontrolled environment with interacting element. An observed interaction performance is evaluated against a target interaction performance to identify an improvement objective. The autonomous agent may be an autonomous robot, a virtual actor, a non-playing character, or a decision agent.