@article{
 title = {A review of generalized planning},
 type = {article},
 year = {2019},
 volume = {34},
 websites = {https://doi.org/10.1017/S0269888918000231},
 id = {41559f3d-3e10-3e17-a92d-be9bec6c7451},
 created = {2019-03-14T08:10:09.791Z},
 file_attached = {true},
 profile_id = {de5765e4-e253-3166-8178-333c824974ba},
 last_modified = {2019-07-24T12:30:29.124Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Generalized planning studies the representation, computation and evaluation of solutions that are valid for multiple planning instances. These are topics studied since the early days of AI. However, in recent years, we are experiencing the appearance of novel formalisms to compactly represent generalized planning tasks, the solutions to these tasks (called generalized plans) and efficient algorithms to compute generalized plans. The paper reviews recent advances in generalized planning and relates them to existing planning formalisms, such as planning with domain control knowledge and approaches for planning under uncertainty, that also aim at generality.},
 bibtype = {article},
 author = {Jiménez, Sergio and Segovia-Aguas, Javier and Jonsson, Anders},
 doi = {10.1017/S0269888918000231},
 journal = {The Knowledge Engineering Review}
}

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