Some Experiments with Real-Time Decision Algorithms
Abstract
Real-time Decision algorithms are a class of incremental resource-bounded [Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence diagrams. We present a test domain for real-time decision algorithms, and the results of experiments with several Real-time Decision Algorithms in this domain. The results demonstrate high performance for two algorithms, a decision-evaluation variant of Incremental Probabilisitic Inference [D'Ambrosio, 93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95], PK-reduced. We discuss the implications of these experimental results and explore the broader applicability of these algorithms.
Cite
Text
D'Ambrosio and Burgess. "Some Experiments with Real-Time Decision Algorithms." Conference on Uncertainty in Artificial Intelligence, 1996.Markdown
[D'Ambrosio and Burgess. "Some Experiments with Real-Time Decision Algorithms." Conference on Uncertainty in Artificial Intelligence, 1996.](https://mlanthology.org/uai/1996/daposambrosio1996uai-some/)BibTeX
@inproceedings{daposambrosio1996uai-some,
title = {{Some Experiments with Real-Time Decision Algorithms}},
author = {D'Ambrosio, Bruce and Burgess, Scott},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1996},
pages = {194-202},
url = {https://mlanthology.org/uai/1996/daposambrosio1996uai-some/}
}