Improving the Robustness and Encoding Complexity of Behavioural Clones
Abstract
The aim of behavioural cloning is to synthesize artificial controllers that are robust and comprehensible to human understanding. To attain the two objectives we propose the use of the Incremental Correction model that is based on a closed-loop control strategy to model the reactive aspects of human control skills. We have investigated the use of three different representations to encode the artificial controllers: univariate decision trees as induced by C4.5; multivariate decision and regression trees as induced by cart and; clausal theories induced by an Inductive Logic Programming (ILP) system. We obtained an increase in robustness and a lower complexity of the controllers when compared with results using other models. The controllers synthesized by cart revealed to be the most robust. The ILP system produced the simpler encodings.
Cite
Text
Camacho and Brazdil. "Improving the Robustness and Encoding Complexity of Behavioural Clones." European Conference on Machine Learning, 2001. doi:10.1007/3-540-44795-4_4Markdown
[Camacho and Brazdil. "Improving the Robustness and Encoding Complexity of Behavioural Clones." European Conference on Machine Learning, 2001.](https://mlanthology.org/ecmlpkdd/2001/camacho2001ecml-improving/) doi:10.1007/3-540-44795-4_4BibTeX
@inproceedings{camacho2001ecml-improving,
title = {{Improving the Robustness and Encoding Complexity of Behavioural Clones}},
author = {Camacho, Rui and Brazdil, Pavel},
booktitle = {European Conference on Machine Learning},
year = {2001},
pages = {37-48},
doi = {10.1007/3-540-44795-4_4},
url = {https://mlanthology.org/ecmlpkdd/2001/camacho2001ecml-improving/}
}