Domain Independent Approaches for Finding Diverse Plans
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2016-2021.
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.
@inproceedings{ijcai2007p325,
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 = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2016--2021},
year = {2007},
url = {https://ijcai.org/proceedings/2007/325},
}
TY - CPAPER TI - Domain Independent Approaches for Finding Diverse Plans AU - Srivastava, Biplav AU - Nguyen, Tuan Anh AU - Gerevini, Alfonso AU - Kambhampati, Subbarao AU - Do, Minh Binh AU - Serina, Ivan T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2016 EP - 2021 UR - https://ijcai.org/proceedings/2007/325 ER -Copied
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