Improving Search Through Diversity
Abstract
Adding diversity to symbolic search techniques has not been explored in artificial intelligence. Adding a diversity criterion provides us with a powerful new mechanism for finding global maxima in complex search spaces and helps to alleviate the problem of premature convergence to local maxima. A theoretical analysis is presented of issues in diversity searching which previously haven't been addressed, and a domain-independent diversity-search algorithm for practical breadth-first searching is developed. Empirical results of an implementation in the CRESUS expert system for intelligent cash-management confirm that diversity can significantly improve the solution quality of symbolic searchers.
Cite
Text
Shell et al. "Improving Search Through Diversity." AAAI Conference on Artificial Intelligence, 1994.Markdown
[Shell et al. "Improving Search Through Diversity." AAAI Conference on Artificial Intelligence, 1994.](https://mlanthology.org/aaai/1994/shell1994aaai-improving/)BibTeX
@inproceedings{shell1994aaai-improving,
title = {{Improving Search Through Diversity}},
author = {Shell, Peter and Rubio, Juan Antonio Hernandez and Barro, Gonzalo Quiroga},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1994},
pages = {1323-1328},
url = {https://mlanthology.org/aaai/1994/shell1994aaai-improving/}
}