On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief Networks
Artificial Intelligence
2013-04-05 v1
Abstract
We introduce a new heuristic algorithm for the problem of finding minimum size loop cutsets in multiply connected belief networks. We compare this algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower bounds on the performance of these algorithms with respect to one another and with respect to optimal. We demonstrate that no heuristic algorithm for this problem cam be guaranteed to produce loop cutsets within a constant difference from optimal. We discuss experimental results based on randomly generated networks, and discuss future work and open questions.
Cite
@article{arxiv.1304.1113,
title = {On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief Networks},
author = {Jonathan Stillman},
journal= {arXiv preprint arXiv:1304.1113},
year = {2013}
}
Comments
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)