Symbol Acquisition for Probabilistic High-Level Planning
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3619-3626.
Abstract
We introduce a framework that enables an agent to autonomously learn its own symbolic representation of a low-level, continuous environment. Propositional symbols are formalized as names for probability distributions, providing a natural means of dealing with uncertain representations and probabilistic plans. We determine the symbols that are sufficient for computing the probability with which a plan will succeed, and demonstrate the acquisition of a symbolic representation in a computer game domain.
@inproceedings{ijcai2015p509,
title = {Symbol Acquisition for Probabilistic High-Level Planning},
author = {Konidaris, George Dimitri and Kaelbling, Leslie Pack and Lozano-Perez, Tomas},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {3619--3626},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/509},
}
TY - CPAPER TI - Symbol Acquisition for Probabilistic High-Level Planning AU - Konidaris, George Dimitri AU - Kaelbling, Leslie Pack AU - Lozano-Perez, Tomas T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 3619 EP - 3626 UR - https://ijcai.org/proceedings/2015/509 ER -Copied
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