IJCAI 2020poster0 citations
Optimal Planning Modulo Theories
Francesco Leofante, Enrico Giunchiglia, Erika Ábráham, Armando Tacchella
Abstract
We consider the problem of planning with arithmetic theories, and focus on generating optimal plans for numeric domains with constant and state-dependent action costs. Solving these problems efficiently requires a seamless integration between propositional and numeric reasoning. We propose a novel approach that leverages Optimization Modulo Theories (OMT) solvers to implement a domain-independent optimal theory-planner. We present a new encoding for optimal planning in this setting and we evaluate our approach using well-known, as well as new, numeric benchmarks.
Planning and Scheduling: Planning AlgorithmsConstraints and SAT: Satisfiability Modulo Theories
BibTeX
@inproceedings{ijcai2020p571,
title = {Optimal Planning Modulo Theories},
author = {Leofante, Francesco and Giunchiglia, Enrico and Ábráham, Erika and Tacchella, Armando},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {4128--4134},
year = {2020},
month = {7},
note = {Main track},
doi = {10.24963/ijcai.2020/571},
url = {https://doi.org/10.24963/ijcai.2020/571},
}