@article{Cecílio Magnaguagno_Meneguzzi_2020, title={Semantic Attachments for HTN Planning}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/6548}, DOI={10.1609/aaai.v34i06.6548}, abstractNote={<p>Hierarchical Task Networks (HTN) planning uses a decomposition process guided by domain knowledge to guide search towards a planning task. While many HTN planners allow calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls. Conversely, the classical planners that are capable of using external calls (often called <em>semantic attachments</em>) during planning are limited to generating a fixed number of ground operators at problem grounding time. We formalize <em>Semantic Attachments</em> for HTN planning using semi coroutines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner, such as numerical results from a robotics simulator. The resulting planner then uses such coroutines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables). We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.</p>}, number={06}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cecílio Magnaguagno, Maurício and Meneguzzi, Felipe}, year={2020}, month={Apr.}, pages={9933-9940} }