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.
CITATION STYLE
Leofante, F., Giunchiglia, E., Ábrahám, E., & Tacchella, A. (2020). Optimal planning modulo theories. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2021-January, pp. 4128–4134). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2020/571
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