Introspection mechanisms are employed in agent architectures to improve agent performance. However, there is currently no approach to introspection that makes automatic adjustments at multiple levels in the implemented agent system. Our novel multi-level introspection framework can be used to automatically adjust architectural configurations based on the introspection results at the agent, infrastructure, and component level. We demonstrate the utility of such adjustments in a concrete implementation on a robot where the high-level goal of the robot is used to automatically configure the vision system in a way that minimizes resource consumption while improving overall task performance.
@inproceedings{krauseetal12aaai,
title={Crossing Boundaries: Multi-Level Introspection in a Complex Robotic Architecture for Automatic Performance Improvements},
author={Evan Krause and Paul Schermerhorn and Matthias Scheutz},
year={2012},
booktitle={Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence},
url={https://hrilab.tufts.edu/publications/krauseetal12aaai.pdf}
}