Behavior Bounding: Toward Effective Comparisons of Agents & Humans
Abstract
In this paper, we examine methods for comparing human and agent behavior. The results of such a comparison can be used to validate a computer model of human behavior, score a Turning test, or guide an intelligent tutoring system. We introduce behavior bounding, an automated model-based approach for behavior comparison. We identify how this approach can be used with both human and agent behavior. We demonstrate that it requires minimal human effort to use, and that it is efficient when working with complex agents. Finally, we show empirical results indicating that this approach is effective at identifying behavioral problems in certain types of agents and that it has superior performance when compared against two benchmarks.
Cite
Text
Wallace and Laird. "Behavior Bounding: Toward Effective Comparisons of Agents & Humans." International Joint Conference on Artificial Intelligence, 2003.Markdown
[Wallace and Laird. "Behavior Bounding: Toward Effective Comparisons of Agents & Humans." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/wallace2003ijcai-behavior/)BibTeX
@inproceedings{wallace2003ijcai-behavior,
title = {{Behavior Bounding: Toward Effective Comparisons of Agents & Humans}},
author = {Wallace, Scott A. and Laird, John E.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2003},
pages = {727-732},
url = {https://mlanthology.org/ijcai/2003/wallace2003ijcai-behavior/}
}