On Learning Action Costs from Input Plans
<|reference_start|>On Learning Action Costs from Input Plans: Most of the work on learning action models focus on learning the actions' dynamics from input plans. This allows us to specify the valid plans of a planning task. However, very little work focuses on learning action costs, which in turn allows us to rank the different plans. In this paper we introduce a new problem: that of learning the costs of a set of actions such that a set of input plans are optimal under the resulting planning model. To solve this problem we present $LACFIP^k$, an algorithm to learn action's costs from unlabeled input plans. We provide theoretical and empirical results showing how $LACFIP^k$ can successfully solve this task.<|reference_end|>
@article{morales2024on,
    title={On Learning Action Costs from Input Plans},
    author={Marianela Morales, Alberto Pozanco, Giuseppe Canonaco, Sriram
  Gopalakrishnan, Daniel Borrajo, Manuela Veloso},
    journal={arXiv preprint arXiv:2408.10889},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10889},
    primaryClass={cs.AI}
}