Decreasing Uncertainty in Planning with State Prediction
Abstract
In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machine-learning techniques. We demonstrate the effectiveness of our approach, showing that it enhances the scalability of our planners, and leads to less time spent on sensing actions.
Cite
Text
Krivic et al. "Decreasing Uncertainty in Planning with State Prediction." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/282Markdown
[Krivic et al. "Decreasing Uncertainty in Planning with State Prediction." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/krivic2017ijcai-decreasing/) doi:10.24963/IJCAI.2017/282BibTeX
@inproceedings{krivic2017ijcai-decreasing,
title = {{Decreasing Uncertainty in Planning with State Prediction}},
author = {Krivic, Senka and Cashmore, Michael and Magazzeni, Daniele and Ridder, Bram and Szedmák, Sándor and Piater, Justus H.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {2032-2038},
doi = {10.24963/IJCAI.2017/282},
url = {https://mlanthology.org/ijcai/2017/krivic2017ijcai-decreasing/}
}