Anytime Synthetic Projection: Maximizing the Probability of Goal Satisfaction
Abstract
This paper presents a projection algorithm for incremental control rule synthesis. The algorithm synthesizes an initial set of goal-achieving control rules using a combination of situation probability and estimated remaining work as a search heuristic. This set of control rules has a certain probability of satisfying the given goal. The probability is incrementally increased by synthesizing additional control rules to handle error situations the execution system is likely to encounter when following the initial control rules. By using situation probabilities the algorithm achieves a computationally effective balance between the limited robustness of triangle tables and the absolute robustness of universal plans.
Cite
Text
Drummond and Bresina. "Anytime Synthetic Projection: Maximizing the Probability of Goal Satisfaction." AAAI Conference on Artificial Intelligence, 1990.Markdown
[Drummond and Bresina. "Anytime Synthetic Projection: Maximizing the Probability of Goal Satisfaction." AAAI Conference on Artificial Intelligence, 1990.](https://mlanthology.org/aaai/1990/drummond1990aaai-anytime/)BibTeX
@inproceedings{drummond1990aaai-anytime,
title = {{Anytime Synthetic Projection: Maximizing the Probability of Goal Satisfaction}},
author = {Drummond, Mark and Bresina, John L.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1990},
pages = {138-144},
url = {https://mlanthology.org/aaai/1990/drummond1990aaai-anytime/}
}