Learning Temporal Plan Preferences from Examples: An Empirical Study

Seimetz, Valentin; Eifler, Rebecca; Hoffmann, Jörg

doi:10.24963/ijcai.2021/572

Learning Temporal Plan Preferences from Examples: An Empirical Study

Valentin Seimetz, Rebecca Eifler, Jörg Hoffmann

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 4160-4166.

https://doi.org/10.24963/ijcai.2021/572

Keywords

Planning and Scheduling: Planning and Scheduling

Abstract

Temporal plan preferences are natural and important in a variety of applications. Yet users often find it difficult to formalize their preferences. Here we explore the possibility to learn preferences from example plans. Focusing on one preference at a time, the user is asked to annotate examples as good/bad. We leverage prior work on LTL formula learning to extract a preference from these examples. We conduct an empirical study of this approach in an oversubscription planning context, using hidden target formulas to emulate the user preferences. We explore four different methods for generating example plans, and evaluate performance as a function of domain and formula size. Overall, we find that reasonable-size target formulas can often be learned effectively.

Valentin Seimetz, Rebecca Eifler, and Jörg Hoffmann. 2021. Learning Temporal Plan Preferences from Examples: An Empirical Study. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4160–4166. https://doi.org/10.24963/ijcai.2021/572

@inproceedings{ijcai2021p572,
  title     = {Learning Temporal Plan Preferences from Examples: An Empirical Study},
  author    = {Seimetz, Valentin and Eifler, Rebecca and Hoffmann, Jörg},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4160--4166},
  year      = {2021},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2021/572},
  url       = {https://doi.org/10.24963/ijcai.2021/572},
}

TY  - CPAPER
TI  - Learning Temporal Plan Preferences from Examples: An Empirical Study
AU  - Seimetz, Valentin
AU  - Eifler, Rebecca
AU  - Hoffmann, Jörg
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4160
EP  - 4166
DO  - 10.24963/ijcai.2021/572
UR  - https://doi.org/10.24963/ijcai.2021/572
ER  -