COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning
Abstract
In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the COMPOSER system which embodies a probabilistic solution to the utility problem. We outline the statistical foundations of our approach and compare it against four other approaches which appear in the literature.
Cite
Text
Gratch and DeJong. "COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning." AAAI Conference on Artificial Intelligence, 1992.Markdown
[Gratch and DeJong. "COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning." AAAI Conference on Artificial Intelligence, 1992.](https://mlanthology.org/aaai/1992/gratch1992aaai-composer/)BibTeX
@inproceedings{gratch1992aaai-composer,
title = {{COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning}},
author = {Gratch, Jonathan and DeJong, Gerald},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1992},
pages = {235-240},
url = {https://mlanthology.org/aaai/1992/gratch1992aaai-composer/}
}