Optimal Planning Modulo Theories

Francesco Leofante, Enrico Giunchiglia, Erika Ábrahám, Armando Tacchella

IJCAI 2020 pp. 4128-4134

doi:10.24963/IJCAI.2020/571 /ijcai/2020/leofante2020ijcai-optimal/

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.

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Text

Leofante et al. "Optimal Planning Modulo Theories." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/571

Markdown

[Leofante et al. "Optimal Planning Modulo Theories." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/leofante2020ijcai-optimal/) doi:10.24963/IJCAI.2020/571

BibTeX

@inproceedings{leofante2020ijcai-optimal,
  title     = {{Optimal Planning Modulo Theories}},
  author    = {Leofante, Francesco and Giunchiglia, Enrico and Ábrahám, Erika and Tacchella, Armando},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {4128-4134},
  doi       = {10.24963/IJCAI.2020/571},
  url       = {https://mlanthology.org/ijcai/2020/leofante2020ijcai-optimal/}
}