@inproceedings{22a0fae3641c41d9b303f21edd7d0f58,
title = "Operator counting heuristics for probabilistic planning",
abstract = "For the past 25 years, heuristic search has been used to solve domain-independent probabilistic planning problems, but with heuristics that determinise the problem and ignore precious probabilistic information. In this paper, we present a generalization of the operator-counting family of heuristics to Stochastic Shortest Path problems (SSPs) that is able to represent the probability of the actions outcomes. Our experiments show that the equivalent of the net change heuristic in this generalized framework obtains significant run time and coverage improvements over other state-of-the-art heuristics in different planners.",
author = "Felipe Trevizan and Sylvie Thi{\'e}baux and Patrik Haslum",
note = "Publisher Copyright: {\textcopyright} 2018 International Joint Conferences on Artificial Intelligence.All right reserved.; 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 ; Conference date: 13-07-2018 Through 19-07-2018",
year = "2018",
doi = "10.24963/ijcai.2018/758",
language = "English",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "5384--5388",
editor = "Jerome Lang",
booktitle = "Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018",
address = "United States",
}