Domain Independent Approaches for Finding Diverse Plans
Abstract
In many planning situations, a planner is required to return a diverse set of plans satisfying the same goals which will be used by the external systems collectively. We take a domain-independent approach to solving this problem. We propose different domain independent distance functions among plans that can provide meaningful insights about the diversity in the plan set. We then describe how two representative state-of-the-art domain independent planning approaches -- one based on compilation to CSP, and the other based on heuristic local search -- can be adapted to produce diverse plans. We present empirical evidence demonstrating the effectiveness of our approaches.
Cite
Text
Srivastava et al. "Domain Independent Approaches for Finding Diverse Plans." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Srivastava et al. "Domain Independent Approaches for Finding Diverse Plans." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/srivastava2007ijcai-domain/)BibTeX
@inproceedings{srivastava2007ijcai-domain,
title = {{Domain Independent Approaches for Finding Diverse Plans}},
author = {Srivastava, Biplav and Nguyen, Tuan Anh and Gerevini, Alfonso and Kambhampati, Subbarao and Do, Minh Binh and Serina, Ivan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {2016-2022},
url = {https://mlanthology.org/ijcai/2007/srivastava2007ijcai-domain/}
}