@article{Wu_Sheldon_Zilberstein_2014, title={Rounded Dynamic Programming for Tree-Structured Stochastic Network Design}, volume={28}, url={https://ojs.aaai.org/index.php/AAAI/article/view/8761}, DOI={10.1609/aaai.v28i1.8761}, abstractNote={ <p> We develop a fast approximation algorithm called rounded dynamic programming (RDP) for stochastic network design problems on directed trees. The underlying model describes phenomena that spread away from the root of a tree, for example, the spread of influence in a hierarchical organization or fish in a river network. Actions can be taken to intervene in the network—for some cost—to increase the probability of propagation along an edge. Our algorithm selects a set of actions to maximize the overall spread in the network under a limited budget. We prove that the algorithm is a fully polynomial-time approximation scheme (FPTAS), that is, it finds (1−ε)-optimal solutions in time polynomial in the input size and 1/ε. We apply the algorithm to the problem of allocating funds efficiently to remove barriers in a river network so fish can reach greater portions of their native range. Our experiments show that the algorithm is able to produce near-optimal solutions much faster than an existing technique. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wu, Xiaojian and Sheldon, Daniel and Zilberstein, Shlomo}, year={2014}, month={Jun.} }